31537878 jp-morgan-quantitative-guide-to-world-cup-2010

Europe Equity Research 18 May 2010

England to Win the World Cup!

A Quantitative Guide to the 2010 World Cup

Equity Quant EUROPE

Matthew BurgessAC

(44-20) 7325-1496 [email protected]

J.P. Morgan Securities Ltd.

Marco DionAC

(44-20) 7325-8647 [email protected]

J.P. Morgan Securities Ltd.

Equity Quant EMERGING MARKETS Steve Malin (852) 2800 8568 [email protected]

J.P. Morgan Securities (Asia Pacific) Limited

Robert Smith (852) 2800 8569 [email protected]

J.P. Morgan Securities (Asia Pacific) Limited

Equity Quant AUSTRALIA Thomas Reif (61-2) 9220-1473 [email protected]

J.P. Morgan Securities Australia Limited

Berowne Hlavaty (61-2) 9220-1591 [email protected]

J.P. Morgan Securities Australia Limited

See page 67 for analyst certification and important disclosures, including non-US aJ.P. Morgan does and seeks to do business with companies covered in its research reports. As a result, inhave a conflict of interest that could affect the objectivity of this report. Investors should consider this repinvestment decision.

• Quant Models are mathematical methods built to efficiently screen and identify stocks.

• They are based on information and data (analyst upgrades, valuation metrics etc) proven to help predict stock returns.

• Having developed a rather successful Quant Model over the years, we intend to introduce it to our readers and also use its methodology to apply it to a fruitful field for statistics: Football and the World Cup.

• In this Model, we focus on market prices, FIFA Ranking, historical results, our J.P. Morgan Team Strength Indicator etc to come up with a mathematical model built to predict match results.

• Ultimately our Model indicates Brazil as being the strongest team taking part in the tournament. However, due to the fixture schedule our Model predicts the following final outcome:

- 3rd: Netherlands

- 2nd: Spain

- World Cup Winners: England

• Alternatively, we point out that the 3 favourite teams (from market prices recorded on 30 April of 3.9-to-1 for Spain, 5-to-1 for Brazil and 5.4-to-1 for England) represent a 52.5% probability of winning the World Cup.

Table 1: World Cup Model “Score” Model Score Model Score

Brazil 1.68 United States 0.01 Spain 1.53 Uruguay -0.06 England 0.91 Slovakia -0.13 Netherlands 0.63 Cameroon -0.18 Argentina 0.48 Australia -0.27 Slovenia 0.47 Ghana -0.29 France 0.47 Nigeria -0.29 Italy 0.43 Switzerland -0.37 Ivory Coast 0.35 Denmark -0.52 Portugal 0.30 Paraguay -0.55 Chile 0.24 Honduras -0.63 Germany 0.13 Korea Republic -0.76 Algeria 0.12 New Zealand -0.81 Serbia 0.03 South Africa -0.92 Greece 0.03 Japan -0.96 Mexico 0.02 Korea DPR -1.11

Source: www.tip-ex.com, fifa.com, J.P. Morgan

Figure 1: J.P. Morgan Cazenove Multi-Factor Quant Model: Long-only vs MSCI World

0100200300400500600700800

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09

Source: MSCI, IBES, Factset, J.P. Morgan

Whilst this report should be taken with a pinch of salt, we find it an interesting exercise and an ideal opportunity to lightheartedly explain Quantitative techniques and demystify the typical Quant framework.

nalyst disclosures. vestors should be aware that the firm may ort as only a single factor in making their

2


Matthew Burgess (44-20) 7325-1496 [email protected]

Marco Dion (44-20) 7325-8647 [email protected]

Table of Contents Introduction ..............................................................................4 Methodology .............................................................................5 What is Quant?.............................................................................................................5 What information is our mathematical Model using? ..................................................6 Our “Valuation” metrics...........................................................7 FIFA World Ranking ...................................................................................................7 “Market” Valuations ....................................................................................................9 Our “Price Trend” metrics .........................................................................................10 Trend in Probability to Win .......................................................................................11 Trend in FIFA’s Ranking...........................................................................................12 Our “Market and Analyst Sentiment” metrics......................13 Result Expectations....................................................................................................13 Recent Team Shape ...................................................................................................14 Our “Fundamentals” metrics ................................................16 Consistency in Market Sentiment ..............................................................................16 J.P. Morgan Cazenove Success Ratio Indicator.........................................................18 Creating a World Cup Quant Model ......................................20 Quant Model creation ................................................................................................20 Ranking produced by our World Cup Model.............................................................21 How to deal with draws and penalty shoot-outs?.......................................................21 The World Cup Wall Chart .....................................................23 Conclusion..............................................................................25 Value..........................................................................................................................59 Price Momentum........................................................................................................60 Growth/Earnings........................................................................................................61 Quality .......................................................................................................................62 Multi Factor Model....................................................................................................63 Current Long Opportunities .......................................................................................64

3




Appendices Appendix I: Fixtures, Prices and Rankings..........................26 France ........................................................................................................................26 Mexico .......................................................................................................................27 South Africa...............................................................................................................28 Uruguay .....................................................................................................................29 Argentina ...................................................................................................................30 Greece ........................................................................................................................31 Korea Republic ..........................................................................................................32 Nigeria .......................................................................................................................33 Algeria .......................................................................................................................34 England ......................................................................................................................35 Slovenia .....................................................................................................................36 USA ...........................................................................................................................37 Australia.....................................................................................................................38 Germany ....................................................................................................................39 Ghana.........................................................................................................................40 Serbia .........................................................................................................................41 Cameroon...................................................................................................................42 Denmark ....................................................................................................................43 Japan ..........................................................................................................................44 Netherlands ................................................................................................................45 Italy ............................................................................................................................46 New Zealand ..............................................................................................................47 Paraguay ....................................................................................................................48 Slovakia .....................................................................................................................49 Brazil..........................................................................................................................50 Ivory Coast.................................................................................................................51 Korea DPR.................................................................................................................52 Portugal......................................................................................................................53 Chile...........................................................................................................................54 Honduras....................................................................................................................55 Spain ..........................................................................................................................56 Switzerland ................................................................................................................57 Appendix II: Quant Factor Analysis - Equity Model.............58 Appendix III: Z-Score Normalisation - Getting Technical! ..65

4




Introduction

As the big kick-off approaches, we present a Quantitative Guide to the World Cup!

With many investors overwhelmed or instantly turned off by the mere mention of the word ‘Quant’, we intend to present a simple Quant methodology applied to a field outside of finance: sports and football in general.

Our goal is indeed to highlight potential World Cup winners by applying Quantitative/mathematical methodology traditionally used with balance-sheet, valuations and consensus information to data from the football world.

To do so, we focus on data including:

• probabilities to win from a range of bookmakers and exchanges

• official FIFA World Rankings

• results from previous World Cup tournaments and qualifying competitions

• etc

Our goal is to construct a mathematical Model1, similar to ones used by the Quant community to pick up stocks, and run the resulting numbers through a World Cup ‘wall chart’.

We then identify who will win the World Cup according to this Quantitative framework.

As a handy reference guide, we also provide fixtures and trends in both probabilities to win and FIFA World Rankings for all countries.

Whilst this report should be taken with a pinch of salt, we find it an interesting exercise and an ideal opportunity to lightheartedly present some simple Quantitative techniques within an easy to understand and topical framework.

1 Also known as “Multi-Factor Quant Model”.

Quantitative framework to identify possible World Cup winner!

Handy reference guide to fixtures, prices and rankings

5




Methodology

What is Quant?

Quantitative Analysis (“Quant”) is an investment methodology based on data and mathematical formula to identify Long/Short trading opportunities.

While to some, Quant sounds overwhelming complex and overly scientific (even ‘black box’), it is simply a way of putting together different efficient elements of information.

Typically, those elements of information are valuation metrics, price trends, analyst opinion, quality of the balance-sheet etc (all information which is generally used – consciously or unconsciously – by more fundamental investors when making investment decisions).

In practice, Quants tend to use 4 types of information in their mathematical models:

1. Valuation metrics

2. Market and Analyst sentiment

3. Company fundamentals

4. Price trends

Like most Quants, we have over the years created a “Quant Model” integrating those different sources of information.

This is simply a mathematical Model which relies on data and market statistics to produce Long and Short trading ideas.

With this Model being relatively successful1, and as the sporting field being highly numerical (match scores, win/loss ratios, probability to win etc being available), we decided to translate our Model into the football field focusing primarily on the World Cup.

1 See Appendix for the backtesting results.

6




What information is our mathematical Model using?

As previously mentioned, we have over the years developed a rather successful Quant Model.

This mathematical model relies on the following information:

Figure 2: J.P. Morgan Cazenove Quant stock-picking Model1

VALUATION METRICS MARKET & ANALYST SENTIMENT- PE vs the market - Recent change in analyst sentiment- PE vs the sector - Recent change in analyst growth expectations- Forecast growth - Recent change in analyst recommendations

COMPANY FUNDAMENTALS PRICE TREND- ROE - Long term trend- Company Risk - Short term trend

Source: J.P. Morgan

Running through statistics we consider make sense in the sporting world, we decided to “translate” this Model into a football-specific Model. We do so as follows using the below statistics:

Figure 3: J.P. Morgan Cazenove Quant world cup-picking Model

"VALUATION" METRICS "MARKET & ANALYST" SENTIMENT- "Market" Valuations - Result Expectations - FIFA World Ranking - Recent Team Shape

"COMPANY FUNDAMENTALS" PRICE TREND- Consistency in Market Sentiment - Trend in probability to win- J.P. Morgan Success Ratio Indicator - Trend in FIFA's Ranking

Source: J.P. Morgan

1 See “The New & Improved Q-Snapshot” (September 2008) for more info.

7




Our “Valuation” metrics

Quant Models often use Valuation metrics as a source of information.

They intuitively make sense as investors do indeed care about valuations and use valuation metrics to differentiate between stocks.

Backtesting numbers also prove it is worth taking valuations into consideration when making investment decisions (ie, cheap stocks have a tendency to outperform expensive stocks in the long run).

Typically Quant Models use Price to Book, PE, Price-to-sales etc.

When we created our World Cup Model we decided to focus on FIFA World Ranking and “Market” Valuations.

FIFA World Ranking World Ranking Points

One could consider that the official FIFA World Ranking1 points as a reasonable “Valuation” metric.

This calculation identifies successful countries and takes into account:

1. Match Result: Win/Lose/Draw

2. Match Status: Friendly/Qualifier/World Cup

3. Opposition Strength: (200 - latest ranking position)/100, subject to a minimum value of 0.5.

4. Regional Strength: The average of the regional/confederation 'strength' of the 2 teams.

5. Assessment Period: Results over the past 4 years are included. More recent results are assigned a higher weighting in the calculation.

In fact, the methodology used by FIFA to assign World Ranking Points is a simple Quantitative model in itself. 1 http://www.fifa.com/worldfootball/ranking/

FIFA already uses a simple Quantitative model to calculate World Ranking Points

8




Table 2: FIFA World Ranking

Country FIFA Ranking

Points FIFA Rank Country FIFA Ranking

Points FIFA Rank Brazil 1611 1 Nigeria 883 20 Spain 1565 2 Australia 883 20 Portugal 1249 3 Slovenia 860 23 Netherlands 1221 4 Switzerland 854 26 Italy 1184 5 Ivory Coast 846 27 Germany 1107 6 Paraguay 822 30 Argentina 1084 7 Algeria 821 31 England 1068 8 Ghana 802 32 France 1044 10 Denmark 767 35 Greece 968 12 Slovakia 742 38 United States 950 14 Honduras 727 40 Chile 948 15 Japan 674 45 Serbia 944 16 Korea Republic 619 47 Mexico 936 17 New Zealand 413 78 Uruguay 902 18 South Africa 369 90 Cameroon 887 19 Korea DPR 292 106 Source: fifa.com (as of 30 April 2010)

The chart below however shows that there are a couple of divergences worth noting while looking at the FIFA ranking and the probability of winning.

Figure 4: FIFA World Ranking vs. probability of winning

Korea DPR South AfricaNew Zealand Korea RepublicJapanHonduras Slov akia Denmark GhanaAlgeria Paraguay Côte dIv oireSw itzerlandSlov eniaAustraliaNigeria CameroonUruguayMex ico Serbia ChileUnited StatesGreece France

EnglandArgentinaGermany

ItalyNetherlandsPortugal SpainBrazil

Probability of Winning (Rank)

Wor

ld Ra

nking

Poin

ts (R

ank)

Source: fifa.com, tip-ex, J.P. Morgan

As the chart points out, Portugal, Netherlands and Greece offer a disagreement with high FIFA World Ranking and low indicated probability to win the World Cup.

England, Argentina and Ivory Coast also offer disagreement with a low World Ranking and an indicated high probability of winning.

According to the FIFA World Ranking Factor, Brazil, Spain Netherlands and Portugal are most likely to win the World Cup.

Top countries according FIFA World Ranking:

Brazil, Spain, Netherlands, Portugal

9




“Market” Valuations Probability of winning the World Cup

For our second “Valuation metric”, we decided to focus on “Market” Valuations as expressed by a country’s probability of winning the World Cup.

Those are calculated from the underlying odds offered by exchanges and fixed odds bookmakers (i.e. the market price)1 as a valuation metric.

Table 3: “Market” Valuations (“win only” market)

Market Price (Probability)

Exchange (Probability) Market Price Exchange

Spain 20.2% 19.2% 4.9 to 1 5.2 to 1 Brazil 16.7% 16.1% 6.0 to 1 6.2 to 1 England 15.6% 13.8% 6.4 to 1 7.3 to 1 Argentina 10.6% 9.5% 9.4 to 1 10.5 to 1 Italy 7.5% 6.5% 13.4 to 1 15.5 to 1 Germany 7.0% 6.5% 14.2 to 1 15.5 to 1 Netherlands 7.0% 5.9% 14.2 to 1 17.0 to 1 France 5.9% 5.0% 17.0 to 1 20.0 to 1 Portugal 4.1% 2.9% 24.4 to 1 34.0 to 1 Ivory Coast 3.5% 3.1% 28.4 to 1 32.0 to 1 Chile 2.1% 1.3% 48.2 to 1 75 to 1 Paraguay 2.0% 1.0% 50 to 1 100 to 1 Serbia 1.6% 1.2% 64 to 1 85 to 1 Ghana 1.3% 1.3% 75 to 1 80 to 1 Mexico 1.3% 0.9% 75 to 1 110 to 1 United States 1.3% 1.2% 75 to 1 85 to 1 Cameroon 1.1% 0.7% 93 to 1 140 to 1 Uruguay 1.1% 0.7% 94 to 1 140 to 1 Nigeria 1.0% 0.6% 97 to 1 170 to 1 Denmark 1.0% 0.6% 104 to 1 170 to 1 Australia 0.9% 0.7% 116 to 1 140 to 1 Greece 0.9% 0.4% 117 to 1 240 to 1 South Africa 0.8% 0.7% 126 to 1 150 to 1 Switzerland 0.6% 0.4% 181 to 1 280 to 1 Japan 0.5% 0.2% 201 to 1 510 to 1 Slovakia 0.5% 0.3% 211 to 1 390 to 1 Korea Republic 0.5% 0.4% 221 to 1 275 to 1 Slovenia 0.5% 0.2% 221 to 1 483 to 1 Algeria 0.2% 0.2% 414 to 1 570 to 1 Honduras 0.2% 0.1% 621 to 1 1,000 to 1 Korea DPR 0.1% 0.1% 1,201 to 1 1,000 to 1 New Zealand 0.1% 0.1% 1,901 to 1 1,000 to 1

Source: www.tip-ex.com, J.P. Morgan (as of 30 April 2010)

1 From www.tip-ex.com, decimal prices using Betfair for exchange prices and the average from a list of 5 fixed odds bookmakers.

Probability =1 / (Market Price)

10




Regardless of whether we look at probabilities from traditional market makers or betting exchanges, we (unsurprisingly) find the countries ordered in a similar manner with only a couple of countries appearing out of sync on the 2 lists.

According to “Market” Valuations Factor: Spain, Brazil, England and Argentina are the most likely to win the World Cup.

Our “Price Trend” metrics

Prices reflect information in the market and provide an opinion in terms of investors’ preference towards a stock.

Backtesting numbers also prove that while in disagreement with the efficient market theory, price trends provide important information about future stock performance (stocks trending up having a tendency to outperform stocks trending down in the long run).

Typically Quant Models use 12 months Price Trend, 1 month Price Trend, RSI, technical indicators, etc

Using the World Cup data we have compiled, there are a few ways in which we can replicate these Price Trends.

We came up with 2 Factors for Price Trend: Trend in Probability to Win and Trend in FIFA’s Ranking.

Top countries according to the “Market” Valuation Factor:

Spain, Brazil, England, Argentina

11




Trend in Probability to Win Change in Probability of Winning

To discover which country’s probability of winning has increased/decreased the most over a given time period, we can look at a simple change in the market price/probability of a country winning the World Cup.

We calculate the probabilities at a given point in time by taking the average probability from a range of market makers1.

Consequently, we calculate the Trend in Probability to Win for 3 and 6 month durations.

Table 4: Trend in Probability to Win2

6mth Trend in

Probability to Win 3mth Trend in

Probability to Win Slovenia 53% -10% France 25% -11% Ivory Coast 20% -7% Greece 16% -11% Uruguay 15% 0% Spain 12% 6% Argentina 9% 21% England 6% -6% United States 5% 4% Italy 5% -8% Nigeria 3% -13% Cameroon 2% -8% Mexico 1% 4% Korea Republic 1% 0% Chile -3% -12% Ghana -7% 0% Brazil -8% -2% Netherlands -8% 1% Slovakia -11% -6% Honduras -15% -10% Germany -18% -5% South Africa -19% 3% Paraguay -19% -20% Serbia -19% 6% Switzerland -20% -10% Portugal -22% 7% Australia -22% -5% Denmark -23% -12% Japan -30% -10% Korea DPR - -5% Algeria - -9% New Zealand - -26% Source: www.Tip-Ex.com, J.P. Morgan (as of 30 April 2010)

1 Information kindly provided by www.tip-ex.com 2 6 month history of odds was not available for Korea DPR, Algeria and New Zealand.

Top countries according to Trend in Probability to Win:

Slovenia, France, Ivory Coast, Greece

12




According to the Trend in Probability to Win Factor: Slovenia, France1, Ivory Coast and Greece are the most attractive options, having received the greatest increase in probability over the past 6 months.

Trend in FIFA’s Ranking Change in FIFA’s World Ranking Points

To a similar extent, we can calculate trend in the FIFA World Ranking points over a given period – our Trend in FIFA’s Ranking metric.

We display each of the 12, 6 and 3 month Factors in the table below but decided to focus on the (longer term) 12 month trend in FIFA’s Ranking (as investors would do with regards to Price Momentum).

Table 5: Trend in FIFA’s Ranking

12mth Chg FIFA

Ranking 6mth Chg FIFA





Ranking Algeria 64% 5% 0% United States 6% -7% -3% Slovenia 63% 30% 12% France 4% 0% -7% Serbia 33% 6% 3% Switzerland 3% -11% -8% Slovakia 30% -2% -1% Korea 0% -8% -1% Chile 28% 4% 1% England -1% -3% -1% Brazil 28% -1% 3% Cameroon -1% -7% -14% Portugal 22% 20% 6% New Zealand -3% 8% 3% Australia 22% 4% 2% Japan -5% -8% -5% Ivory Coast 20% -6% -9% Spain -6% -4% -4% Mexico 19% 4% 1% Netherlands -7% -9% -5% Nigeria 13% 16% 4% Italy -8% -3% -2% Greece 12% 5% -5% Paraguay -9% -6% 2% Ghana 12% 8% 7% Korea DPR -10% -19% -22% Denmark 9% -8% -6% Argentina -11% -2% 0% Uruguay 8% 8% -1% Germany -19% -5% -6% Honduras 8% -4% -1% South Africa -21% -3% -2% Source: fifa.com, J.P. Morgan (as of 30 April 2010)

According to the Trend in FIFA’s Ranking, Algeria, Slovenia, Serbia and Slovakia have the biggest change in World Ranking Points and should be preferred.

1 For France, It should be noted that a significant amount of this momentum is associated with price changes around their Play-Off fixture vs. Ireland (18 November 2009) which may lead to some exaggeration.

Top countries according to the Trend in FIFA’s ranking:

Algeria, Slovenia, Serbia, Slovakia

13




Our “Market and Analyst Sentiment” metrics

Quant Models often use “sentiment” based information. “Sentiment” metrics are based on translations of investor reaction to market events (like earnings announcement change in dividend policies) and change in analyst expectations about a company.

There is indeed a strong behavioural argument for investors to follow or invest in stocks on which consensus displays strong opinion or on which analysts have recently changed their recommendations, their growth forecasts etc.

Backtesting tests also prove that, amongst other things, over the long run stocks which got recently upgraded by consensus (and/or highly ranked analysts) outperform stocks which got downgraded.

When we created our World Cup Model we decided to focus on country’s Result Expectations and on our Recent Team Shape.

Result Expectations Result expectations from a team’s track record in past World Cups

As an approach to calculating World Cup “expectations”, we developed a simple scoring technique to reward countries based purely on their historical World Cup track record as it helps greatly to understand a country’s result “expectations”.

We call this metric the country’s Result Expectations. This metric could be associated in the Quant space to “Historical past growth”.

For each historical World Cup played, 50 points are therefore assigned to the winner, 25 to the runner up, 15 to 3rd place and 10 to 4th place.

14




Table 6: Result Expectations

Top 4 Finish

Result Expectation Price

Top 4 Finish

Result Expectation Price

Brazil 10 340 6.0 Mexico 0 0 75 Germany 11 305 14.2 Ghana 0 0 75 Italy 8 275 13.4 Cameroon 0 0 93 Argentina 3 125 9.4 Nigeria 0 0 97 France 5 115 17.0 Denmark 0 0 104 Uruguay 3 70 94 Australia 0 0 116 England 2 60 6.4 Greece 0 0 117 Netherlands 3 60 14.2 South Africa 0 0 126 Portugal 2 25 24.4 Switzerland 0 0 181 Chile 1 15 48.2 Japan 0 0 201 Spain 1 10 4.9 Slovakia 0 0 211 Korea Republic 1 10 221 Slovenia 0 0 221 Ivory Coast 0 0 28.4 Algeria 0 0 413 Paraguay 0 0 50 Honduras 0 0 621 Serbia 0 0 65 Korea DPR 0 0 1,201

United States 0 0 75 New Zealand 0 0 1,901

Source: J.P. Morgan

Using this methodology we, unsurprisingly, find at the top of the country’s Result Expectations metric the likes of Brazil, Germany, Italy and Argentina.

Recent Team Shape Average Ranking Points won per match

To incorporate recent form and shape (what could be associated with “Recent Growth”), we use the Recent Team Shape metrics.

This is calculated by taking the average FIFA World Ranking points earned over a given time period.

We provide the data for the last 3, 6 and 12 month period but focus on the team “shape” over the 12 months.

Top countries according to Result Expectations Factor:

Brazil, Germany, Italy, Argentina

Top countries according to Recent Team Shape Factor:

Netherlands, Spain, Brazil, Portugal

15




Table 7: Recent Team shape

12mth Recent Team Shape






Netherlands 1,121 1,283 0 Denmark 646 483 600 Spain 1,039 846 866 United States 646 658 1,014 Brazil 894 777 184 Algeria 626 601 546 Portugal 859 936 999 Mexico 618 857 516 Chile 811 676 1,169 Uruguay 592 631 665 Slovakia 794 743 619 New Zealand 592 592 592 England 779 790 465 Paraguay 566 1,188 1,103 Serbia 752 597 656 Ghana 563 329 159 Italy 747 844 673 Argentina 528 520 1,040 Switzerland 734 715 543 Slovenia 520 635 518 Côte dIvoire 731 577 204 Nigeria 477 562 682 France 729 618 759 Honduras 470 532 370 Greece 702 630 875 Japan 446 0 0 Germany 690 689 906 Korea DPR 422 0 0 Cameroon 689 956 854 Korea Republic 411 0 0 Australia 662 0 0 South Africa 0 0 0 Source: fifa.com, J.P. Morgan (as of 31 December 2009)

We only include World Cup qualifying and World Cup Finals results in this calculation as opposed to all international fixtures used in the official FIFA rankings.

As there have been no World Cup fixtures in 2010, we take this data from 31 December 2009.

According to 12 month Recent Team Shape1, Netherlands, Spain, Brazil and Portugal are the preferred countries2.

1 With South Africa not having played a qualifying fixture over the past 12 months, Korea Republic, Korea DPR, Japan and Honduras rank the worst on Recent Form, although again shorting/laying these countries has little profit margin. 2 Interestingly, Argentina scores poorly on this metric, implying that they have recently fared poorly against stronger opposition.

16




Our “Fundamentals” metrics

Lastly, we wanted to mention that Equity Quant Models indeed often use Fundamentals/Balance Sheet as source of information.

We are all aware that fundamentals do matter and can help greatly in separating sound companies from riskier counterparties.

Backtesting numbers also prove that metrics like ROE, ROA, leverage etc are worth taking in consideration when making investment decisions (“better” stocks outperforming less good expensive stocks over the long run).

When we created our World Cup Model we decided to focus on Consistency in Market Sentiment and on our “J.P. Morgan Cazenove Success Ratio” Indicator.

Consistency in Market Sentiment Agreement within the “market” on the probability of a team winning.

Using “market” information and prices, the Consistency in Market Sentiment metric aims to look at the uniformity of probability offered across a range of “probability to win” providers1 .

This ensures that we reward both a high probability of winning and a high level of agreement between probability providers.

Our metric is calculated as:

Average(Probability of Winning) / (Max(Probability of Winning)-Min(Probability of Winning))

1 Using 5 fixed odd bookmakers (from www.tip-ex.com)

17




Whilst this may not provide us with any concrete information as to who may win the World Cup (although it can be argued that if “price providers” are in agreement on the favorites’ probability to win then one can be more confident that the country will progress in the tournament) it will provide us with an idea as to where potential issues and opportunities exist.

This also gives us confidence in the country probability and the lack of surprise at play.

Table 8: Consistency in Market Sentiment

Consistency in

Market Sentiment Latest Odds

Consistency in Market Sentiment

Latest Odds

Brazil 33.33 6.0 Portugal 3.2 24.4 England 13.80 6.4 Italy 3.0 13.4 Spain 9.20 4.9 South Africa 2.5 126 Ivory Coast 8.87 28.4 Uruguay 2.3 94 Argentina 8.59 9.4 Chile 2.2 48 Germany 6.87 14.2 Japan 1.9 201 Ghana 5.39 75 Korea Republic 1.9 221 United States 5.39 75 Slovakia 1.9 211 Mexico 5.19 75 Greece 1.5 117 Nigeria 4.84 97 Denmark 1.4 104 Cameroon 4.45 93 Paraguay 1.4 50 Australia 4.24 116 Slovenia 1.4 221 France 4.13 17 Algeria 1.3 413 Netherlands 3.94 14.2 Honduras 1.0 621 Switzerland 3.42 181 New Zealand 1.0 1,901 Serbia 3.39 64 Korea DPR 0.8 1,201 Source: tip-ex, J.P. Morgan (30 April 2010)

According to the Consistency in Market Sentiment Factor we can be most confident that Brazil, England, Spain and Ivory Coast provide less risk in terms of result surprise and that they are correctly priced1.

1 It is interesting to note that Brazil, England and Spain account for 52.5% of total probability of winning the World Cup, also constituting the top 3 positions according to the above “Consistency in Market Sentiment” Factor. One can conclude that it would be a surprise if one of these nations were to not win the World Cup.

Top countries according to Consistency in Market Sentiment:

Brazil, England, Spain, Ivory Coast

18




J.P. Morgan Cazenove Success Ratio Indicator Win Ratio adjusted for opponent’s strength

A likened translation of company fundamentals is to check the strength of a country’s Win Ratio by scaling for the strength of the opponent played.

This metric could be associated to the “strength of a balance-sheet” Factor.

The ratio is calculated by computing the Win Ratio (ie proportion of wins from the total number of games played) and scaling it by the FIFA World Ranking (ie higher for a strong team and small for a weak team).

Below we present the Win Ratio of team over various periods (table 9).

Those numbers are then adjusted according to opponent’s strength as displayed next page on Table 10).

Table 9: Win Ratio

12mth Win

Ratio 6mth Win

Ratio 3mth Win

Ratio 12mth Win

Ratio 6mth Win

Ratio 3mth Win

Ratio Netherlands 100% (5) 100% (1) 0% (0) Brazil 63% (8) 50% (4) 0% (2) Spain 100% (6) 100% (4) 100% (2) Australia 60% (5) 0% (0) 0% (0) England 83% (6) 67% (3) 50% (2) Mexico 60% (10) 80% (5) 50% (2) Germany 83% (6) 75% (4) 50% (2) Slovenia 57% (7) 80% (5) 67% (3) Ivory Coast 80% (5) 50% (2) 0% (1) Denmark 57% (7) 25% (4) 50% (2) Portugal 75% (8) 83% (6) 100% (4) Argentina 50% (8) 50% (4) 100% (2)Algeria 71% (7) 75% (4) 67% (3) Greece 50% (8) 50% (6) 75% (4) Cameroon 67% (6) 100% (4) 100% (2) Honduras 50% (10) 60% (5) 50% (2) Ghana 67% (6) 33% (3) 0% (2) New Zealand 50% (2) 50% (2) 50% (2) Italy 67% (6) 75% (4) 50% (2) Nigeria 50% (6) 67% (3) 100% (2)Serbia 67% (6) 33% (3) 50% (2) Paraguay 43% (7) 100% (3) 100% (1)Slovakia 67% (6) 50% (4) 50% (2) Uruguay 40% (10) 50% (6) 50% (4) France 67% (9) 57% (7) 75% (4) Japan 40% (5) 0% (0) 0% (0) Switzerland 67% (6) 50% (4) 50% (2) Korea DPR 40% (5) 0% (0) 0% (0) United States 67% (9) 75% (4) 100% (1) Korea Republic 40% (5) 0% (0) 0% (0) Chile 63% (8) 50% (4) 100% (2) South Africa 0% (0) 0% (0) 0% (0) Source: fifa.com, J.P. Morgan (31 December 2009 - number of games played in brackets)

19




Table 10: J.P. Morgan Cazenove Success Ratio Indicator Success Ratio Success Ratio

Netherlands 586.2 Switzerland 375.2 Spain 536.3 Germany 363.9 Chile 503.7 Cameroon 356.4 Brazil 481.6 Honduras 342.5 England 464.9 Greece 341.6 Serbia 430.4 Uruguay 335.5 Portugal 423.3 Australia 331.0 United States 419.6 Denmark 329.1 Slovakia 412.0 Ghana 324.9 Argentina 402.9 Slovenia 317.6 Paraguay 395.2 New Zealand 262.0 Algeria 395.1 Japan 245.3 Mexico 387.8 Nigeria 233.8 Ivory Coast 385.6 Korea DPR 192.1 France 382.5 Korea Republic 172.0 Italy 380.2 South Africa 0.0 Source: fifa.com, J.P. Morgan

According to our J.P. Morgan Cazenove Success Ratio Indicator, Netherlands, Spain, Chile and Brazil should be preferred1.

Having defined the underlying metrics, we next merge them into our mathematical (“Multi-Factor Quant Model”) framework.

The methodology is explained in detail in the following section.

1 It is also worth noting that the following have the lowest number of defeats over the last 12 months: Japan, Korea DPR, United States, Slovenia, Uruguay, Mexico, Honduras, Paraguay, Argentina

Success Ratio= Win Ratio x Average Opposition Strength

Top countries according to Success Ratio:

Netherlands, Spain, Chile, Brazil

20




Creating a World Cup Quant Model

No World Cup analysis would be complete without a traditional World Cup ‘wall chart’!

Having defined the relevant metrics and criteria identifying potential World Cup winners, we decided to create an appropriate weighting scheme and mathematical formula to generate our match result predictions.

In the following section we therefore create our simple Quant Model and illustrate how the resulting scores can be used to calculate individual match outcomes and ultimately result in a World Cup winner.

Quant Model creation

As the below table illustrates, we allocate different weights to the various metrics we use.

Figure 5: World Cup Multi Factor Model "VALUATION" METRICS -- 40% "MARKET & ANALYST" SENTIMENT -- 15%

- "Market" Valuations -- 50% - Result Expectations -- 33%- FIFA World Ranking -- 50% - Recent Team Shape -- 67%

"COMPANY FUNDAMENTALS" -- 15% PRICE TREND -- 30%- Consistency in Market Sentiment -- 50% - Trend in probability to win -- 50%- J.P. Morgan Success Ratio Indicator -- 50% - Trend in FIFA's Ranking -- 50%

Source: J.P. Morgan

Whilst the weights of the underlying Factors were decided arbitrarily, we argue that they are indicative of the importance of each of the underlying datasets (similar to what Quant managers would do with Quant Factors).

With “Market” Valuations and FIFA World Ranking clearly being the driving metrics in identifying the strongest candidates, we assign them a significant proportion (20%) of the Model weight.

We also see the change in these 2 metrics (ie their “Momentum”) as strong indicators. We therefore allocate these favourable weights with the Model.

21




Ranking produced by our World Cup Model

The metrics presented below, once aggregated following the methodology presented in appendix II1, culminate in a single score for each country. This “score” helps us understand match scores and winners.

The table below ranks the teams according to the overall score generated by our mathematical Model.

Table 11: World Cup Model

Model Score Groups (from 1 to 8) Model Score Groups (from 1

to 8) Brazil 1.68 8 United States 0.01 4 Spain 1.53 8 Uruguay -0.06 4 England 0.91 8 Slovakia -0.13 4 Netherlands 0.63 8 Cameroon -0.18 4 Argentina 0.48 7 Australia -0.27 3 Slovenia 0.47 7 Ghana -0.29 3 France 0.47 7 Nigeria -0.29 3 Italy 0.43 7 Switzerland -0.37 3 Ivory Coast 0.35 6 Denmark -0.52 2 Portugal 0.30 6 Paraguay -0.55 2 Chile 0.24 6 Honduras -0.63 2 Germany 0.13 6 Korea Republic -0.76 2 Algeria 0.12 5 New Zealand -0.81 1 Serbia 0.03 5 South Africa -0.92 1 Greece 0.03 5 Japan -0.96 1 Mexico 0.02 5 Korea DPR -1.11 1

Source: tip-ex.com, fifa.com, J.P. Morgan

According to this Model, Brazil is the strongest team in the World Cup competition!

How to deal with draws and penalty shoot-outs?

However, it is a (more than distinct) possibility that matches will result in a draw during the World Cup. Using just the above model scores, this would be an impossibility.

Consequently, we also allocated the different countries into groups (or “performance buckets” from 1 to 8) that we will use as our primarily source of country ranking.

1 See Appendix II: “Z-Score Normalization – Getting Technical !”

Top countries according to World Cup Model

Brazil, Spain, England, Netherlands

22




We can then run the Model through each of the 64 scheduled fixtures assigning win, lose or draw to each match depending on the Group the country is in.

In the case of a drawn match in the knockout phase, we decided to introduce a methodology defining a “Scoring Ability” and a “Goalkeeper Ability”.

As a penalty shoot-out introduces a further level of uncertainty, we decided not only to focus on the ability of a team to score goals; we also focused on the goalkeeper’s ability to stop them.

We therefore create a Penalty Shoot-out metric that we calculate as follows:

Figure 6: “Penalty Shoot-out” metric: combining “Ability to Score” with “Goalkeeper Ability”

Nbr of goals scored Nbr of goals conceded---------------------------------- * 50% - --------------------------------------- * 50%

Nbr of games played Nbr of games played( ) ( )

Source: J.P. Morgan

As the table below shows England have an impressive score on our “Penalty Shoot-out” metric.

Table 12: Penalty Shoot-out metric: combining “Ability to Score” with “Goal-keeper Ability” (from -3 to +3)

Scoring Ability

Goalkeeper Ability

Penalty Shoot Out Metric

Scoring Ability

Goalkeeper Ability

Penalty Shoot Out Metric

England 3.00 0.30 1.65 Italy -0.04 -0.10 -0.07 Spain 1.88 0.69 1.29 Japan -0.34 0.13 -0.11 Netherlands 0.58 1.68 1.13 Ghana -0.30 0.04 -0.13 Germany 1.49 0.69 1.09 Switzerland -0.04 -0.49 -0.27 Ivory Coast 1.04 0.51 0.78 Slovakia 0.73 -1.28 -0.28 Cameroon 0.18 1.35 0.77 Korea DPR -1.99 1.26 -0.36 New Zealand 0.34 0.69 0.52 Algeria -0.99 0.24 -0.37 Nigeria -0.30 1.02 0.36 Greece -0.14 -0.62 -0.38 Australia -0.89 1.54 0.33 France -0.30 -0.62 -0.46 Portugal -0.46 1.02 0.28 Mexico 0.34 -1.28 -0.47 United States 1.02 -0.59 0.22 Honduras -0.08 -1.28 -0.68 Slovenia -0.18 0.51 0.17 Paraguay -0.79 -0.59 -0.69 Brazil 0.02 0.25 0.14 Uruguay -0.62 -1.48 -1.05 Denmark -0.42 0.69 0.13 Chile -0.08 -2.16 -1.12 Serbia 0.73 -0.49 0.12 South Africa -1.90 -0.62 -1.26 Korea -0.48 0.69 0.11 Argentina -1.04 -1.72 -1.38 Source: J.P. Morgan

We can then populate our World Cup ‘wall chart’!

23




The World Cup Wall Chart Figure 7: Group Phase

Jun 11, 2010 14:00 South Africa W Mexico Johannesburg - JSCJun 11, 2010 18:30 Uruguay W France Cape TownJun 12, 2010 14:00 Argentina W Nigeria Johannesburg - JEPJun 12, 2010 11:30 Korea Republic W Greece Nelson Mandela BayJun 12, 2010 18:30 England W USA RustenburgJun 13, 2010 11:30 Algeria W Slovenia PolokwaneJun 13, 2010 18:30 Germany W Australia DurbanJun 13, 2010 14:00 Serbia W Ghana Tshwane/PretoriaJun 14, 2010 11:30 Netherlands W Denmark Johannesburg - JSCJun 14, 2010 14:00 Japan W Cameroon Mangaung / BloemfonteinJun 14, 2010 18:30 Italy W Paraguay Cape TownJun 15, 2010 11:30 New Zealand W Slovakia RustenburgJun 15, 2010 14:00 Côte d'Ivoire D D Portugal Nelson Mandela BayJun 15, 2010 18:30 Brazil W Korea DPR Johannesburg - JEPJun 16, 2010 11:30 Honduras W Chile NelspruitJun 16, 2010 14:00 Spain W Switzerland DurbanJun 16, 2010 18:30 South Africa W Uruguay Tshwane/PretoriaJun 17, 2010 18:30 France W Mexico PolokwaneJun 17, 2010 14:00 Greece W Nigeria Mangaung / BloemfonteinJun 17, 2010 11:30 Argentina W Korea Republic Johannesburg - JSCJun 18, 2010 11:30 Germany W Serbia Nelson Mandela BayJun 18, 2010 14:00 Slovenia W USA Johannesburg - JEPJun 18, 2010 18:30 England W Algeria Cape TownJun 19, 2010 14:00 Ghana D D Australia RustenburgJun 19, 2010 11:30 Netherlands W Japan DurbanJun 19, 2010 18:30 Cameroon W Denmark Tshwane/PretoriaJun 20, 2010 11:30 Slovakia W Paraguay Mangaung / BloemfonteinJun 20, 2010 14:00 Italy W New Zealand NelspruitJun 20, 2010 18:30 Brazil W Côte d'Ivoire Johannesburg - JSCJun 21, 2010 11:30 Portugal W Korea DPR Cape TownJun 21, 2010 14:00 Chile W Switzerland Nelson Mandela BayJun 21, 2010 18:30 Spain W Honduras Johannesburg - JEPJun 22, 2010 14:00 Mexico W Uruguay RustenburgJun 22, 2010 14:00 France W South Africa Mangaung / BloemfonteinJun 22, 2010 18:30 Nigeria W Korea Republic DurbanJun 22, 2010 18:30 Greece W Argentina PolokwaneJun 23, 2010 14:00 Slovenia W England Nelson Mandela BayJun 23, 2010 14:00 USA W Algeria Tshwane/PretoriaJun 23, 2010 18:30 Ghana W Germany Johannesburg - JSCJun 23, 2010 18:30 Australia W Serbia NelspruitJun 24, 2010 14:00 Slovakia W Italy Johannesburg - JEPJun 24, 2010 14:00 Paraguay W New Zealand PolokwaneJun 24, 2010 18:30 Denmark W Japan RustenburgJun 24, 2010 18:30 Cameroon W Netherlands Cape TownJun 25, 2010 14:00 Portugal W Brazil DurbanJun 25, 2010 14:00 Korea DPR W Côte d'Ivoire NelspruitJun 25, 2010 18:30 Chile W Spain Tshwane/PretoriaJun 25, 2010 18:30 Switzerland W Honduras Mangaung / Bloemfontein

Source: tip-ex, fifa.com, J.P. Morgan (W=Win, D= Draw). Kick Off in London time)

24




Figure 8: Knockout Phase

Jun 26, 2010 14:00 France W Greece Nelson Mandela BayJun 26, 2010 18:30 England W Serbia RustenburgJun 27, 2010 14:00 Germany W Slovenia Mangaung / BloemfonteinJun 27, 2010 18:30 Argentina W Mexico JohannesburgJun 28, 2010 14:00 Netherlands W Slovakia DurbanJun 28, 2010 18:30 Brazil W Chile JohannesburgJun 29, 2010 14:00 Italy W Cameroon Tshwane/PretoriaJun 29, 2010 18:30 Spain W Portugal Cape Town

Jul 2, 2010 14:00 Netherlands W (pen) Brazil Nelson Mandela BayJul 2, 2010 18:30 France W England JohannesburgJul 3, 2010 14:00 Argentina W (pen) Slovenia Cape TownJul 3, 2010 18:30 Italy W Spain Johannesburg

Jul 6, 2010 18:30 England W (pen) Netherlands Cape TownJul 7, 2010 18:30 Slovenia W Spain Durban

Jul 10, 2010 18:30 Netherlands W Slovenia Nelson Mandela Bay

Jul 11, 2010 18:30 England W (pen) Spain Johannesburg

Round of 16

Quarter Final

Semi Final

3rd Place Play Off

Final

Source: tip-ex, fifa.com, J.P. Morgan (W=Win, D=Draw, W (pen) = Victory via penalties. Kick Off in London time.

25




Conclusion

Quant analysis is a practice often considered as “too complex” by a large part of the investment community.

In this document we explained that, on the contrary, Quant is far from complex as Quants merely try to remove human based opinions when they make investment decisions.

Instead they use information and data points they consider relevant to investment in a systematic and efficient manner.

Once they have found data sets thought to exert influence over future returns, they backtest them and make sure they can be used on a day-to-day basis to generate alpha.

As Quants use only numerical/statistical data for their market analysis, it seemed that sound Quant/mathematical Models could be used in fields outside Finance to make accurate predictions.

With the amount of statistical information now available for Football fans, we thought it would be a very fruitful ground for investigation.

We therefore decided to “translate” our successful stock-picking Quant Model and adapt it to predict the outcome of the World Cup matches and ultimately provide the World Cup winner.

As explained in the document, we focused on very intuitive data (comprising recent team performance, FIFA ranking, probability to win etc).

Ultimately, we used our mathematical Model and applied it on a match by match basis and predicted winners.

Whilst our Model points towards Brazil as being the strongest team to take part in the World Cup, our “World Cup Wall Chart” indicates that thanks to the actual fixtures determined by the schedule, we believe England will be the winner of the 2010 World Cup.

We also highlight that the 3 favourites according to both our model and market prices (Brazil, Spain and England) offer a combined probability of 52.5% of winning the World Cup (as per prices on 30 April).

26




Appendix I: Fixtures, Prices and Rankings France

Table 13: Qualifying Results Date Home Away 06-Sep-08 Austria 3 - 1 France 10-Sep-08 France 2 - 1 Serbia 11-Oct-08 Romania 2 - 2 France 28-Mar-09 Lithuania 0 - 1 France 01-Apr-09 France 1 - 0 Lithuania 12-Aug-09 Faroe Islands 0 - 1 France 05-Sep-09 France 1 - 1 Romania 09-Sep-09 Serbia 1 - 1 France 10-Oct-09 France 5 - 0 Faroe Islands 14-Oct-09 France 3 - 1 Austria 14-Nov-09 Republic of Ireland 0 - 1 France 18-Nov-09 France 1 - 1 Republic of Ireland

Source: fifa.com, J.P. Morgan

Table 14: World Cup Fixtures – Group A

Date Time Home Away Model

Prediction Fri Jun 11, 2010 18:30 Uruguay v France France

Thu Jun 17, 2010 18:30 France v Mexico France Tue Jun 22, 2010 14:00 France v South Africa France

Source: fifa.com, J.P. Morgan (Times in GMT)

Figure 9: Probability of Winning and FIFA World Ranking, recent history

Latest Odds: Fixed - 17.3; Exchange - 21

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1234567891011

Fix ed Ex change Ranking (rhs)

Source: tip-ex, J.P. Morgan

27




Mexico

Table 15: Qualifying Results Date Home Away 15-Jun-08 Belize 0 - 2 Mexico 21-Jun-08 Mexico 7 - 0 Belize 20-Aug-08 Mexico 2 - 1 Honduras 06-Sep-08 Mexico 3 - 0 Jamaica 10-Sep-08 Mexico 2 - 1 Canada 11-Oct-08 Jamaica 1 - 0 Mexico 15-Oct-08 Canada 2 - 2 Mexico 19-Nov-08 Honduras 1 - 0 Mexico 11-Feb-09 United States 2 - 0 Mexico 28-Mar-09 Mexico 2 - 0 Costa Rica 01-Apr-09 Honduras 3 - 1 Mexico 06-Jun-09 El Salvador 2 - 1 Mexico 10-Jun-09 Mexico 2 - 1 Trinidad and Tobago 12-Aug-09 Mexico 2 - 1 United States 05-Sep-09 Costa Rica 0 - 3 Mexico 09-Sep-09 Mexico 1 - 0 Honduras 10-Oct-09 Mexico 4 - 1 El Salvador 14-Oct-09 Trinidad and Tobago 2 - 2 Mexico




Prediction Fri Jun 11, 2010 14:00 South Africa v Mexico Mexico

Thu Jun 17, 2010 18:30 France v Mexico France Tue Jun 22, 2010 14:00 Mexico v Uruguay Mexico




0.0%

0.5%

1.0%

1.5%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16

1116

2126



28




South Africa

Table 17: Qualifying Results Date Home Away 01-Jun-08 Nigeria 2 - 0 South Africa 07-Jun-08 South Africa 4 - 1 Equatorial Guinea 14-Jun-08 Sierra Leone 1 - 0 South Africa 21-Jun-08 South Africa 0 - 0 Sierra Leone 06-Sep-08 South Africa 0 - 1 Nigeria 11-Oct-08 Equatorial Guinea 0 - 1 South Africa




Prediction Fri Jun 11, 2010 14:00 South Africa v Mexico Mexico

Wed Jun 16, 2010 18:30 South Africa v Uruguay Uruguay Tue Jun 22, 2010 14:00 France v South Africa France



Latest Odds: Fixed - 124; Exchange - 190

0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

121

4161

81101



29




Uruguay

Table 19: Qualifying Results Date Home Away 13-Oct-07 Uruguay 5 - 0 Bolivia 17-Oct-07 Paraguay 1 - 0 Uruguay 18-Nov-07 Uruguay 2 - 2 Chile 21-Nov-07 Brazil 2 - 1 Uruguay 14-Jun-08 Uruguay 1 - 1 Venezuela 17-Jun-08 Uruguay 6 - 0 Peru 06-Sep-08 Colombia 0 - 1 Uruguay 10-Sep-08 Uruguay 0 - 0 Ecuador 11-Oct-08 Argentina 2 - 1 Uruguay 14-Oct-08 Bolivia 2 - 2 Uruguay 28-Mar-09 Uruguay 2 - 0 Paraguay 01-Apr-09 Chile 0 - 0 Uruguay 06-Jun-09 Uruguay 0 - 4 Brazil 10-Jun-09 Venezuela 2 - 2 Uruguay 05-Sep-09 Peru 1 - 0 Uruguay 09-Sep-09 Uruguay 3 - 1 Colombia 10-Oct-09 Ecuador 1 - 2 Uruguay 14-Oct-09 Uruguay 0 - 1 Argentina 14-Nov-09 Costa Rica 0 - 1 Uruguay 18-Nov-09 Uruguay 1 - 1 Costa Rica




Prediction Fri Jun 11, 2010 18:30 Uruguay v France France

Wed Jun 16, 2010 18:30 South Africa v Uruguay Uruguay Tue Jun 22, 2010 14:00 Mexico v Uruguay Mexico




0.0%0.2%0.4%0.6%0.8%1.0%1.2%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

161116212631



30




Argentina

Table 21: Qualifying Results Date Home Away 13-Oct-07 Argentina 2 - 0 Chile 16-Oct-07 Venezuela 0 - 2 Argentina 17-Nov-07 Argentina 3 - 0 Bolivia 20-Nov-07 Colombia 2 - 1 Argentina 15-Jun-08 Argentina 1 - 1 Ecuador 18-Jun-08 Brazil 0 - 0 Argentina 06-Sep-08 Argentina 1 - 1 Paraguay 10-Sep-08 Peru 1 - 1 Argentina 11-Oct-08 Argentina 2 - 1 Uruguay 15-Oct-08 Chile 1 - 0 Argentina 28-Mar-09 Argentina 4 - 0 Venezuela 01-Apr-09 Bolivia 6 - 1 Argentina 06-Jun-09 Argentina 1 - 0 Colombia 10-Jun-09 Ecuador 2 - 0 Argentina 05-Sep-09 Argentina 1 - 3 Brazil 09-Sep-09 Paraguay 1 - 0 Argentina 10-Oct-09 Argentina 2 - 1 Peru 14-Oct-09 Uruguay 0 - 1 Argentina


Table 22: World Cup Fixtures – Group B


Prediction Sat Jun 12, 2010 14:00 Argentina v Nigeria Argentina Thu Jun 17, 2010 11:30 Argentina v Korea Republic Argentina Tue Jun 22, 2010 18:30 Greece v Argentina Argentina



Latest Odds: Fixed - 8.4; Exchange - 8.8

0.0%2.0%4.0%6.0%8.0%

10.0%12.0%14.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

13

57

911



31




Greece

Table 23: Qualifying Results Date Home Away 06-Sep-08 Luxembourg 0 - 3 Greece 10-Sep-08 Latvia 0 - 2 Greece 11-Oct-08 Greece 3 - 0 Moldova 15-Oct-08 Greece 1 - 2 Switzerland 28-Mar-09 Israel 1 - 1 Greece 01-Apr-09 Greece 2 - 1 Israel 05-Sep-09 Switzerland 2 - 0 Greece 09-Sep-09 Moldova 1 - 1 Greece 10-Oct-09 Greece 5 - 2 Latvia 14-Oct-09 Greece 2 - 1 Luxembourg 14-Nov-09 Greece 0 - 0 Ukraine 18-Nov-09 Ukraine 0 - 1 Greece




Prediction Sat Jun 12, 2010 11:30 Korea Republic v Greece Greece Thu Jun 17, 2010 14:00 Greece v Nigeria Greece Tue Jun 22, 2010 18:30 Greece v Argentina Argentina




0.0%0.2%0.4%0.6%0.8%1.0%1.2%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

6

11

16

21



32




Korea Republic

Table 25: Qualifying Results Date Home Away 06-Feb-08 Korea Republic 4 - 0 Turkmenistan 26-Mar-08 Korea DPR 0 - 0 Korea Republic 31-May-08 Korea Republic 2 - 2 Jordan 07-Jun-08 Jordan 0 - 1 Korea Republic 14-Jun-08 Turkmenistan 1 - 3 Korea Republic 22-Jun-08 Korea Republic 0 - 0 Korea DPR 10-Sep-08 Korea DPR 1 - 1 Korea Republic 15-Oct-08 Korea Republic 4 - 1 United Arab Emirates 19-Nov-08 Saudi Arabia 0 - 2 Korea Republic 11-Feb-09 Iran 1 - 1 Korea Republic 01-Apr-09 Korea Republic 1 - 0 Korea DPR 06-Jun-09 United Arab Emirates 0 - 2 Korea Republic 10-Jun-09 Korea Republic 0 - 0 Saudi Arabia 17-Jun-09 Korea Republic 1 - 1 Iran




Prediction Sat Jun 12, 2010 11:30 Korea Republic v Greece Greece Thu Jun 17, 2010 11:30 Argentina v Korea Republic Argentina Tue Jun 22, 2010 18:30 Nigeria v Korea Republic Nigeria




0.0%0.1%0.2%0.3%0.4%0.5%0.6%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1112131415161



33




Nigeria

Table 27: Qualifying Results Date Home Away 01-Jun-08 Nigeria 2 - 0 South Africa 07-Jun-08 Sierra Leone 0 - 1 Nigeria 15-Jun-08 Equatorial Guinea 0 - 1 Nigeria 21-Jun-08 Nigeria 2 - 0 Equatorial Guinea 06-Sep-08 South Africa 0 - 1 Nigeria 11-Oct-08 Nigeria 4 - 1 Sierra Leone 29-Mar-09 Mozambique 0 - 0 Nigeria 07-Jun-09 Nigeria 3 - 0 Kenya 20-Jun-09 Tunisia 0 - 0 Nigeria 06-Sep-09 Nigeria 2 - 2 Tunisia 11-Oct-09 Nigeria 1 - 0 Mozambique 14-Nov-09 Kenya 2 - 3 Nigeria




Prediction Sat Jun 12, 2010 14:00 Argentina v Nigeria Argentina Thu Jun 17, 2010 14:00 Greece v Nigeria Greece Tue Jun 22, 2010 18:30 Nigeria v Korea Republic Nigeria




0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16111621263136



34




Algeria

Table 29: Qualifying Results Date Home Away 31-May-08 Senegal 1 - 0 Algeria 06-Jun-08 Algeria 3 - 0 Liberia 14-Jun-08 Gambia 1 - 0 Algeria 20-Jun-08 Algeria 1 - 0 Gambia 05-Sep-08 Algeria 3 - 2 Senegal 11-Oct-08 Liberia 0 - 0 Algeria 28-Mar-09 Rwanda 0 - 0 Algeria 07-Jun-09 Algeria 3 - 1 Egypt 20-Jun-09 Zambia 0 - 2 Algeria 06-Sep-09 Algeria 1 - 0 Zambia 11-Oct-09 Algeria 3 - 1 Rwanda 14-Nov-09 Egypt 2 - 0 Algeria 18-Nov-09 Algeria 1 - 0 Egypt


Table 30: World Cup Fixtures – Group C


Prediction Sun Jun 13, 2010 11:30 Algeria v Slovenia Slovenia Fri Jun 18, 2010 18:30 England v Algeria England

Wed Jun 23, 2010 14:00 USA v Algeria Algeria Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%0.1%0.1%0.2%0.2%0.3%0.3%0.4%

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16111621263136



35




England

Table 31: Qualifying Results Date Home Away 06-Sep-08 Andorra 0 - 2 England 10-Sep-08 Croatia 1 - 4 England 11-Oct-08 England 5 - 1 Kazakhstan 15-Oct-08 Belarus 1 - 3 England 01-Apr-09 England 2 - 1 Ukraine 06-Jun-09 Kazakhstan 0 - 4 England 10-Jun-09 England 6 - 0 Andorra 09-Sep-09 England 5 - 1 Croatia 10-Oct-09 Ukraine 1 - 0 England 14-Oct-09 England 3 - 0 Belarus




Prediction Sat Jun 12, 2010 18:30 England v USA England Fri Jun 18, 2010 18:30 England v Algeria England

Wed Jun 23, 2010 14:00 Slovenia v England England Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%

5.0%

10.0%

15.0%

20.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

13

57

911



36




Slovenia

Table 33: Qualifying Results Date Home Away 06-Sep-08 Poland 1 - 1 Slovenia 10-Sep-08 Slovenia 2 - 1 Slovakia 11-Oct-08 Slovenia 2 - 0 Northern Ireland 15-Oct-08 Czech Republic 1 - 0 Slovenia 28-Mar-09 Slovenia 0 - 0 Czech Republic 01-Apr-09 Northern Ireland 1 - 0 Slovenia 12-Aug-09 Slovenia 5 - 0 San Marino 09-Sep-09 Slovenia 3 - 0 Poland 10-Oct-09 Slovakia 0 - 2 Slovenia 14-Oct-09 San Marino 0 - 3 Slovenia 14-Nov-09 Russia 2 - 1 Slovenia




Prediction Sat Jun 12, 2010 18:30 England v USA England Fri Jun 18, 2010 14:00 Slovenia v USA Slovenia




0.0%

0.2%

0.4%

0.6%

0.8%

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1112131415161



37




USA

Table 35: Qualifying Results Date Home Away 15-Jun-08 USA 8 - 0 Barbados 22-Jun-08 Barbados 0 - 1 USA 20-Aug-08 Guatemala 0 - 1 USA 06-Sep-08 Cuba 0 - 1 USA 10-Sep-08 USA 3 - 0 Trinidad and Tobago 11-Oct-08 USA 6 - 1 Cuba 15-Oct-08 Trinidad and Tobago 2 - 1 USA 19-Nov-08 USA 2 - 0 Guatemala 11-Feb-09 USA 2 - 0 Mexico 28-Mar-09 El Salvador 2 - 2 USA 01-Apr-09 USA 3 - 0 Trinidad and Tobago 03-Jun-09 Costa Rica 3 - 1 USA 06-Jun-09 USA 2 - 1 Honduras 12-Aug-09 Mexico 2 - 1 USA 05-Sep-09 USA 2 - 1 El Salvador 09-Sep-09 Trinidad and Tobago 0 - 1 USA 10-Oct-09 Honduras 2 - 3 USA




Prediction Sat Jun 12, 2010 18:30 England v USA England Fri Jun 18, 2010 14:00 Slovenia v USA Slovenia




0.0%

0.5%

1.0%

1.5%

2.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

6

11

16

21



38




Australia

Table 37: Qualifying Results Date Home Away 06-Feb-08 Australia 3 - 0 Qatar 26-Mar-08 China PR 0 - 0 Australia 01-Jun-08 Australia 1 - 0 Iraq 07-Jun-08 Iraq 1 - 0 Australia 14-Jun-08 Qatar 1 - 3 Australia 22-Jun-08 Australia 0 - 1 China PR 10-Sep-08 Uzbekistan 0 - 1 Australia 15-Oct-08 Australia 4 - 0 Qatar 19-Nov-08 Bahrain 0 - 1 Australia 11-Feb-09 Japan 0 - 0 Australia 01-Apr-09 Australia 2 - 0 Uzbekistan 06-Jun-09 Qatar 0 - 0 Australia 10-Jun-09 Australia 2 - 0 Bahrain 17-Jun-09 Australia 2 - 1 Japan


Table 38: World Cup Fixtures – Group D


Prediction Sun Jun 13, 2010 18:30 Germany v Australia Germany Sat Jun 19, 2010 14:00 Ghana v Australia Draw

Wed Jun 23, 2010 18:30 Australia v Serbia Serbia Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%0.2%0.4%0.6%0.8%1.0%1.2%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16

1116

2126



39




Germany

Table 39: Qualifying Results Date Home Away 06-Sep-08 Liechtenstein 0 - 6 Germany 10-Sep-08 Finland 3 - 3 Germany 11-Oct-08 Germany 2 - 1 Russia 15-Oct-08 Germany 1 - 0 Wales 28-Mar-09 Germany 4 - 0 Liechtenstein 01-Apr-09 Wales 0 - 2 Germany 12-Aug-09 Azerbaijan 0 - 2 Germany 09-Sep-09 Germany 4 - 0 Azerbaijan 10-Oct-09 Russia 0 - 1 Germany 14-Oct-09 Germany 1 - 1 Finland




Prediction Sun Jun 13, 2010 18:30 Germany v Australia Germany Fri Jun 18, 2010 11:30 Germany v Serbia Germany

Wed Jun 23, 2010 18:30 Ghana v Germany Germany Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%2.0%

4.0%6.0%

8.0%10.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1234567



40




Ghana

Table 41: Qualifying Results Date Home Away 01-Jun-08 Ghana 3 - 0 Libya 08-Jun-08 Lesotho 2 - 3 Ghana 14-Jun-08 Gabon 2 - 0 Ghana 22-Jun-08 Ghana 2 - 0 Gabon 05-Sep-08 Libya 1 - 0 Ghana 11-Oct-08 Ghana 3 - 0 Lesotho 29-Mar-09 Ghana 1 - 0 Benin 07-Jun-09 Mali 0 - 2 Ghana 20-Jun-09 Sudan 0 - 2 Ghana 06-Sep-09 Ghana 2 - 0 Sudan 11-Oct-09 Benin 1 - 0 Ghana 15-Nov-09 Ghana 2 - 2 Mali




Prediction Sun Jun 13, 2010 14:00 Serbia v Ghana Serbia Sat Jun 19, 2010 14:00 Ghana v Australia Draw

Wed Jun 23, 2010 18:30 Ghana v Germany Germany Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%

0.5%

1.0%

1.5%

2.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

11

21

31

41



41




Serbia

Table 43: Qualifying Results Date Home Away

06-Sep-08 Serbia 2 - 0 Faroe Islands 10-Sep-08 France 2 - 1 Serbia 11-Oct-08 Serbia 3 - 0 Lithuania 15-Oct-08 Austria 1 - 3 Serbia 28-Mar-09 Romania 2 - 3 Serbia 06-Jun-09 Serbia 1 - 0 Austria 10-Jun-09 Faroe Islands 0 - 2 Serbia 09-Sep-09 Serbia 1 - 1 France 10-Oct-09 Serbia 5 - 0 Romania 14-Oct-09 Lithuania 2 - 1 Serbia




Prediction Sun Jun 13, 2010 14:00 Serbia v Ghana Serbia Fri Jun 18, 2010 11:30 Germany v Serbia Germany

Wed Jun 23, 2010 18:30 Australia v Serbia Serbia Source: fifa.com, J.P. Morgan (Times in GMT)



0.0%0.5%

1.0%1.5%

2.0%2.5%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

6

11

16

21



42




Cameroon

Table 45: Qualifying Results Date Home Away 31-May-08 Cameroon 2 - 0 Cape Verde Islands 08-Jun-08 Mauritius 0 - 3 Cameroon 14-Jun-08 Tanzania 0 - 0 Cameroon 21-Jun-08 Cameroon 2 - 1 Tanzania 06-Sep-08 Cape Verde Islands 1 - 2 Cameroon 11-Oct-08 Cameroon 5 - 0 Mauritius 28-Mar-09 Togo 1 - 0 Cameroon 07-Jun-09 Cameroon 0 - 0 Morocco 05-Sep-09 Gabon 0 - 2 Cameroon 09-Sep-09 Cameroon 2 - 1 Gabon 10-Oct-09 Cameroon 3 - 0 Togo 14-Nov-09 Morocco 0 - 2 Cameroon


Table 46: World Cup Fixtures – Group E


Prediction Mon Jun 14, 2010 14:00 Japan v Cameroon Cameroon Sat Jun 19, 2010 18:30 Cameroon v Denmark Cameroon Thu Jun 24, 2010 18:30 Cameroon v Netherlands Netherlands




0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

161116212631



43




Denmark

Table 47: Qualifying Results Date Home Away 06-Sep-08 Hungary 0 - 0 Denmark 10-Sep-08 Portugal 2 - 3 Denmark 11-Oct-08 Denmark 3 - 0 Malta 28-Mar-09 Malta 0 - 3 Denmark 01-Apr-09 Denmark 3 - 0 Albania 06-Jun-09 Sweden 0 - 1 Denmark 05-Sep-09 Denmark 1 - 1 Portugal 09-Sep-09 Albania 1 - 1 Denmark 10-Oct-09 Denmark 1 - 0 Sweden 14-Oct-09 Denmark 0 - 1 Hungary




Prediction Mon Jun 14, 2010 11:30 Netherlands v Denmark Netherlands Sat Jun 19, 2010 18:30 Cameroon v Denmark Cameroon Thu Jun 24, 2010 18:30 Denmark v Japan Denmark




0.0%

0.5%

1.0%

1.5%

2.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

11

21

31

41



44




Japan

Table 49: Qualifying Results Date Home Away 06-Feb-08 Japan 4 - 1 Thailand 26-Mar-08 Bahrain 1 - 0 Japan 02-Jun-08 Japan 3 - 0 Oman 07-Jun-08 Oman 1 - 1 Japan 14-Jun-08 Thailand 0 - 3 Japan 22-Jun-08 Japan 1 - 0 Bahrain 06-Sep-08 Bahrain 2 - 3 Japan 15-Oct-08 Japan 1 - 1 Uzbekistan 19-Nov-08 Qatar 0 - 3 Japan 11-Feb-09 Japan 0 - 0 Australia 28-Mar-09 Japan 1 - 0 Bahrain 06-Jun-09 Uzbekistan 0 - 1 Japan 10-Jun-09 Japan 1 - 1 Qatar 17-Jun-09 Australia 2 - 1 Japan




Prediction Mon Jun 14, 2010 14:00 Japan v Cameroon Cameroon Sat Jun 19, 2010 11:30 Netherlands v Japan Netherlands Thu Jun 24, 2010 18:30 Denmark v Japan Denmark




0.0%

0.2%

0.4%

0.6%

0.8%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

111

2131

4151



45




Netherlands

Table 51: Qualifying Results Date Home Away 10-Sep-08 FYR Macedonia 1 - 2 Netherlands 11-Oct-08 Netherlands 2 - 0 Iceland 15-Oct-08 Norway 0 - 1 Netherlands 28-Mar-09 Netherlands 3 - 0 Scotland 01-Apr-09 Netherlands 4 - 0 FYR Macedonia 06-Jun-09 Iceland 1 - 2 Netherlands 10-Jun-09 Netherlands 2 - 0 Norway 09-Sep-09 Scotland 0 - 1 Netherlands




Prediction Mon Jun 14, 2010 11:30 Netherlands v Denmark Netherlands Sat Jun 19, 2010 11:30 Netherlands v Japan Netherlands Thu Jun 24, 2010 18:30 Cameroon v Netherlands Netherlands




0.0%2.0%

4.0%6.0%

8.0%10.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

12233445



46




Italy

Table 53: Qualifying Results Date Home Away 06-Sep-08 Cyprus 1 - 2 Italy 10-Sep-08 Italy 2 - 0 Georgia 11-Oct-08 Bulgaria 0 - 0 Italy 15-Oct-08 Italy 2 - 1 Montenegro 28-Mar-09 Montenegro 0 - 2 Italy 01-Apr-09 Italy 1 - 1 Republic of Ireland 05-Sep-09 Georgia 0 - 2 Italy 09-Sep-09 Italy 2 - 0 Bulgaria 10-Oct-09 Republic of Ireland 2 - 2 Italy 14-Oct-09 Italy 3 - 2 Cyprus


Table 54: World Cup Fixtures – Group F


Prediction Mon Jun 14, 2010 18:30 Italy v Paraguay Italy Sun Jun 20, 2010 14:00 Italy v New Zealand Italy Thu Jun 24, 2010 14:00 Slovakia v Italy Italy




0.0%2.0%

4.0%6.0%

8.0%10.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

12

34

56



47




New Zealand

Table 55: Qualifying Results Date Home Away 17-Oct-07 Fiji 0 - 2 New Zealand 17-Nov-07 Vanuatu 1 - 2 New Zealand 21-Nov-07 New Zealand 4 - 1 Vanuatu 06-Sep-08 New Caledonia 1 - 3 New Zealand 10-Oct-09 Bahrain 0 - 0 New Zealand 14-Nov-09 New Zealand 1 - 0 Bahrain




Prediction Tue Jun 15, 2010 11:30 New Zealand v Slovakia Slovakia Sun Jun 20, 2010 14:00 Italy v New Zealand Italy Thu Jun 24, 2010 14:00 Paraguay v New Zealand Paraguay




0.0%0.0%0.0%0.1%0.1%0.1%0.1%

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

121

4161

81101



48




Paraguay

Table 57: Qualifying Results Date Home Away 13-Oct-07 Peru 0 - 0 Paraguay 17-Oct-07 Paraguay 1 - 0 Uruguay 17-Nov-07 Paraguay 5 - 1 Ecuador 21-Nov-07 Chile 0 - 3 Paraguay 15-Jun-08 Paraguay 2 - 0 Brazil 18-Jun-08 Bolivia 4 - 2 Paraguay 06-Sep-08 Argentina 1 - 1 Paraguay 09-Sep-08 Paraguay 2 - 0 Venezuela 11-Oct-08 Colombia 0 - 1 Paraguay 15-Oct-08 Paraguay 1 - 0 Peru 28-Mar-09 Uruguay 2 - 0 Paraguay 01-Apr-09 Ecuador 1 - 1 Paraguay 06-Jun-09 Paraguay 0 - 2 Chile 10-Jun-09 Brazil 2 - 1 Paraguay 05-Sep-09 Paraguay 1 - 0 Bolivia 09-Sep-09 Paraguay 1 - 0 Argentina 10-Oct-09 Venezuela 1 - 2 Paraguay




Prediction Mon Jun 14, 2010 18:30 Italy v Paraguay Italy Sun Jun 20, 2010 11:30 Slovakia v Paraguay Slovakia Thu Jun 24, 2010 14:00 Paraguay v New Zealand Paraguay




0.0%0.5%

1.0%1.5%

2.0%2.5%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16111621263136



49




Slovakia

Table 59: Qualifying Results Date Home Away 06-Sep-08 Slovakia 2 - 1 Northern Ireland 10-Sep-08 Slovenia 2 - 1 Slovakia 11-Oct-08 San Marino 1 - 3 Slovakia 15-Oct-08 Slovakia 2 - 1 Poland 01-Apr-09 Czech Republic 1 - 2 Slovakia 06-Jun-09 Slovakia 7 - 0 San Marino 05-Sep-09 Slovakia 2 - 2 Czech Republic 09-Sep-09 Northern Ireland 0 - 2 Slovakia 10-Oct-09 Slovakia 0 - 2 Slovenia 14-Oct-09 Poland 0 - 1 Slovakia




Prediction Tue Jun 15, 2010 11:30 New Zealand v Slovakia Slovakia Sun Jun 20, 2010 11:30 Slovakia v Paraguay Slovakia Thu Jun 24, 2010 14:00 Slovakia v Italy Italy




0.0%0.1%0.2%0.3%0.4%0.5%0.6%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

111

2131

4151



50




Brazil

Table 61: Qualifying Results Date Home Away 14-Oct-07 Colombia 0 - 0 Brazil 17-Oct-07 Brazil 5 - 0 Ecuador 18-Nov-07 Peru 1 - 1 Brazil 21-Nov-07 Brazil 2 - 1 Uruguay 15-Jun-08 Paraguay 2 - 0 Brazil 18-Jun-08 Brazil 0 - 0 Argentina 07-Sep-08 Chile 0 - 3 Brazil 10-Sep-08 Brazil 0 - 0 Bolivia 12-Oct-08 Venezuela 0 - 4 Brazil 15-Oct-08 Brazil 0 - 0 Colombia 29-Mar-09 Ecuador 1 - 1 Brazil 01-Apr-09 Brazil 3 - 0 Peru 06-Jun-09 Uruguay 0 - 4 Brazil 10-Jun-09 Brazil 2 - 1 Paraguay 05-Sep-09 Argentina 1 - 3 Brazil 09-Sep-09 Brazil 4 - 2 Chile 11-Oct-09 Bolivia 2 - 1 Brazil 14-Oct-09 Brazil 0 - 0 Venezuela


Table 62: World Cup Fixtures – Group G


Prediction Tue Jun 15, 2010 18:30 Brazil v Korea DPR Brazil Sun Jun 20, 2010 18:30 Brazil v Ivory Coast Brazil Fri Jun 25, 2010 14:00 Portugal v Brazil Brazil



Latest Odds: Fixed - 6; Exchange - 6.2

14.0%15.0%

16.0%17.0%

18.0%19.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1112222



51




Ivory Coast

Table 63: Qualifying Results Date Home Away 01-Jun-08 Ivory Coast 1 - 0 Mozambique 08-Jun-08 Madagascar 0 - 0 Ivory Coast 14-Jun-08 Botswana 1 - 1 Ivory Coast 22-Jun-08 Ivory Coast 4 - 0 Botswana 07-Sep-08 Mozambique 1 - 1 Ivory Coast 11-Oct-08 Ivory Coast 3 - 0 Madagascar 29-Mar-09 Ivory Coast 5 - 0 Malawi 07-Jun-09 Guinea 1 - 2 Ivory Coast 20-Jun-09 Burkina Faso 2 - 3 Ivory Coast 05-Sep-09 Ivory Coast 5 - 0 Burkina Faso 10-Oct-09 Malawi 1 - 1 Ivory Coast




Prediction Tue Jun 15, 2010 14:00 Ivory Coast v Portugal Draw Sun Jun 20, 2010 18:30 Brazil v Ivory Coast Brazil Fri Jun 25, 2010 14:00 Korea DPR v Ivory Coast Ivory Coast




0.0%1.0%

2.0%3.0%

4.0%5.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

161116212631



52




Korea DPR

Table 65: Qualifying Results Date Home Away 06-Feb-08 Jordan 0 - 1 Korea DPR 26-Mar-08 Korea DPR 0 - 0 Korea Republic 02-Jun-08 Turkmenistan 0 - 0 Korea DPR 07-Jun-08 Korea DPR 1 - 0 Turkmenistan 14-Jun-08 Korea DPR 2 - 0 Jordan 22-Jun-08 Korea Republic 0 - 0 Korea DPR 06-Sep-08 United Arab Emirates 1 - 2 Korea DPR 10-Sep-08 Korea DPR 1 - 1 Korea Republic 15-Oct-08 Iran 2 - 1 Korea DPR 11-Feb-09 Korea DPR 1 - 0 Saudi Arabia 28-Mar-09 Korea DPR 2 - 0 United Arab Emirates 01-Apr-09 Korea Republic 1 - 0 Korea DPR 06-Jun-09 Korea DPR 0 - 0 Iran 17-Jun-09 Saudi Arabia 0 - 0 Korea DPR




Prediction Tue Jun 15, 2010 18:30 Brazil v Korea DPR Brazil Mon Jun 21, 2010 11:30 Portugal v Korea DPR Portugal Fri Jun 25, 2010 14:00 Korea DPR v Ivory Coast Ivory Coast




0.0%

0.1%

0.1%

0.2%

0.2%

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

121416181101121



53




Portugal

Table 67: Qualifying Results Date Home Away 06-Sep-08 Malta 0 - 4 Portugal 10-Sep-08 Portugal 2 - 3 Denmark 11-Oct-08 Sweden 0 - 0 Portugal 15-Oct-08 Portugal 0 - 0 Albania 28-Mar-09 Portugal 0 - 0 Sweden 06-Jun-09 Albania 1 - 2 Portugal 05-Sep-09 Denmark 1 - 1 Portugal 09-Sep-09 Hungary 0 - 1 Portugal 10-Oct-09 Portugal 3 - 0 Hungary 14-Oct-09 Portugal 4 - 0 Malta 14-Nov-09 Portugal 1 - 0 Bosnia-Herzegovina 18-Nov-09 Bosnia-Herzegovina 0 - 1 Portugal




Prediction Tue Jun 15, 2010 14:00 Ivory Coast v Portugal Draw Mon Jun 21, 2010 11:30 Portugal v Korea DPR Portugal Fri Jun 25, 2010 14:00 Portugal v Brazil Brazil




0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1

6

11

16

21



54




Chile

Table 69: Qualifying Results Date Home Away 13-Oct-07 Argentina 2 - 0 Chile 17-Oct-07 Chile 2 - 0 Peru 18-Nov-07 Uruguay 2 - 2 Chile 21-Nov-07 Chile 0 - 3 Paraguay 15-Jun-08 Bolivia 0 - 2 Chile 19-Jun-08 Venezuela 2 - 3 Chile 07-Sep-08 Chile 0 - 3 Brazil 10-Sep-08 Chile 4 - 0 Colombia 12-Oct-08 Ecuador 1 - 0 Chile 15-Oct-08 Chile 1 - 0 Argentina 29-Mar-09 Peru 1 - 3 Chile 01-Apr-09 Chile 0 - 0 Uruguay 06-Jun-09 Paraguay 0 - 2 Chile 10-Jun-09 Chile 4 - 0 Bolivia 05-Sep-09 Chile 2 - 2 Venezuela 09-Sep-09 Brazil 4 - 2 Chile 10-Oct-09 Colombia 2 - 4 Chile 14-Oct-09 Chile 1 - 0 Ecuador


Table 70: World Cup Fixtures – Group H


Prediction Wed Jun 16, 2010 11:30 Honduras v Chile Chile Mon Jun 21, 2010 14:00 Chile v Switzerland Chile Fri Jun 25, 2010 18:30 Chile v Spain Spain




0.0%0.5%

1.0%1.5%

2.0%2.5%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

16

1116

2126



55




Honduras

Table 71: Qualifying Results Date Home Away 04-Jun-08 Honduras 4 - 0 Puerto Rico 14-Jun-08 Puerto Rico 2 - 2 Honduras 20-Aug-08 Mexico 2 - 1 Honduras 06-Sep-08 Canada 1 - 2 Honduras 10-Sep-08 Honduras 2 - 0 Jamaica 11-Oct-08 Honduras 3 - 1 Canada 15-Oct-08 Jamaica 1 - 0 Honduras 19-Nov-08 Honduras 1 - 0 Mexico 11-Feb-09 Costa Rica 2 - 0 Honduras 28-Mar-09 Trinidad and Tobago 1 - 1 Honduras 01-Apr-09 Honduras 3 - 1 Mexico 06-Jun-09 United States 2 - 1 Honduras 10-Jun-09 Honduras 1 - 0 El Salvador 12-Aug-09 Honduras 4 - 0 Costa Rica 05-Sep-09 Honduras 4 - 1 Trinidad and Tobago 09-Sep-09 Mexico 1 - 0 Honduras 10-Oct-09 Honduras 2 - 3 United States 14-Oct-09 El Salvador 0 - 1 Honduras




Prediction Wed Jun 16, 2010 11:30 Honduras v Chile Chile Mon Jun 21, 2010 18:30 Spain v Honduras Spain Fri Jun 25, 2010 18:30 Switzerland v Honduras Switzerland




0.0%0.1%

0.1%0.2%

0.2%0.3%

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

111

2131

4151



56




Spain

Table 73: Qualifying Results Date Home Away 06-Sep-08 Spain 1 - 0 Bosnia-Herzegovina 10-Sep-08 Spain 4 - 0 Armenia 11-Oct-08 Estonia 0 - 3 Spain 15-Oct-08 Belgium 1 - 2 Spain 28-Mar-09 Spain 1 - 0 Turkey 01-Apr-09 Turkey 1 - 2 Spain 05-Sep-09 Spain 5 - 0 Belgium 09-Sep-09 Spain 3 - 0 Estonia 10-Oct-09 Armenia 1 - 2 Spain 14-Oct-09 Bosnia-Herzegovina 2 - 5 Spain




Prediction Wed Jun 16, 2010 14:00 Spain v Switzerland Spain Mon Jun 21, 2010 18:30 Spain v Honduras Spain Fri Jun 25, 2010 18:30 Chile v Spain Spain




0.0%5.0%

10.0%15.0%

20.0%25.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

1112222



57




Switzerland

Table 75: Qualifying Results Date Home Away 06-Sep-08 Israel 2 - 2 Switzerland 10-Sep-08 Switzerland 1 - 2 Luxembourg 11-Oct-08 Switzerland 2 - 1 Latvia 15-Oct-08 Greece 1 - 2 Switzerland 28-Mar-09 Moldova 0 - 2 Switzerland 01-Apr-09 Switzerland 2 - 0 Moldova 05-Sep-09 Switzerland 2 - 0 Greece 09-Sep-09 Latvia 2 - 2 Switzerland 10-Oct-09 Luxembourg 0 - 3 Switzerland 14-Oct-09 Switzerland 0 - 0 Israel




Prediction Wed Jun 16, 2010 14:00 Spain v Switzerland Spain Mon Jun 21, 2010 14:00 Chile v Switzerland Chile Fri Jun 25, 2010 18:30 Switzerland v Honduras Switzerland




0.0%0.2%

0.4%0.6%

0.8%1.0%

Sep-09

Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Apr-10

161116212631



58




Appendix II: Quant Factor Analysis - Equity Model

In this section, we illustrate the historical performance of Quantitative (Equity) Factors 'comparable' to those analysed in the World Cup Factor Companion, highlight relevant performance characteristics and provide the latest long/short opportunities arising from the Factor.

In each case, our test universe is MSCI AC World (i.e. including both developed and emerging markets and currently encompassing around 2400 stocks).

We split the universe into deciles (i.e. 10 portfolios), and assign the top 10 % as our long and bottom 10% as our short portfolio, according to Sector Normalised Factor scores.

Portfolios are equally weighted and rebalanced on a monthly basis from December 1993 to date.

Ideally, we look for a monotonic distribution of returns across the portfolios with portfolio 1 outperforming portfolio2, portfolio 2 outperforming portfolio 3.... and portfolio 9 outperforming portfolio 10.

From a statistical perspective we also look at:

Information Coefficient (IC): This sounds more complicated than it actually is. We calculate the IC as the correlation between the model scores at the start of a month and the realised returns come the end of the month. We repeat this for each month in the test take the average over time. An IC in excess of 5% is considered 'good'.

Hit Rate: We look at the percentage of months in which the strategy was successful on a long./short basis – how many months did portfolio 1 outperform portfolio 10? A 'good' strategy will outperform upwards of 65% of the time.

T-Stat: the difference of two means test. This determines how confident we are that there is a significant difference between the return characteristics of portfolio 1 and portfolio 10. A T-Stat > 2 (roughly and set at the 95% confidence limit) implies that we can reject the null hypothesis that ‘there is no significant difference between the two deciles’ and that we are 95% confident that the two deciles are different.

Test universe – MSCI AC World

Universe split into 10 portfolios according to sector normalised Factor scores

Market Neutral and equally weighted portfolios

IC > 5% considered good

Hit Rate in excess of 65% considered good

The larger the T-Stat, the more confidence we have that the two portfolios are different

59




Value 12 month Forward Earnings Yield (P/E) – Long/Short

A pro-rata of consensus FY1 and FY2 forecasts are used to create a 12 month forward earnings estimate. Stocks with the lowest yield are assigned to portfolio 10, highest yield to portfolio 1

Figure 41: Backtest Results Portfolio Spread (Annualised Returns)

0%

5%

10%

15%

20%

1 2 3 4 5 6 7 8 9 10

Information Coefficient

-20%-15%-10%

-5%0%5%

10%15%20%25%

Dec-

93

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Cumulative Returns

0

100

200

300

400

500

600

Base

Nov-

94

Nov-

95

Nov-

96

Nov-

97

Nov-

98

Nov-

99

Nov-

00

Nov-

01

Nov-

02

Nov-

03

Nov-

04

Nov-

05

Nov-

06

Nov-

07

Nov-

08

Nov-

09

12 Month Rolling Returns (Drawdown Analysis)

-30%-20%-10%

0%10%20%30%40%50%60%

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Portfolio Statistics

Port Avg Ann St % OutUniverse Ret Ret Dev Perf.

1 1.5% 16.3% 6% 65%10 0.6% 5.3% 5% 45%

Avg Rank Avg AvgRet IC IC Assets

Universe 0.8% 3.0% 2.4% 2041

Avg Ann Std % OutRet Ret Devn Perf.

Long/Short 0.9% 10.62% 2.6% 62%Avg

T-Stat AssetsLong/Short 4.71 409

Total Test

Portfolio 1 less Portfolio 10Long Short Strategy Statistics

Long Only Returns Relative to Benchmark

0100200300400500600700

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09

Source: MSCI, Factset, J.P. Morgan

60




Price Momentum 12 month Price Momentum – Long/Short

The 12 month Price Momentum Factor is calculated by ranking stocks according to their total return over the past 12 months. Stocks with the highest Price Momentum are assigned to portfolio 1, lowest to portfolio 10.


0%2%4%6%8%

10%12%14%16%

1 2 3 4 5 6 7 8 9 10


-60%-50%-40%-30%-20%-10%

0%10%20%30%

Dec-

93

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Cumulative Returns

0

50

100

150

200

250

Base

Nov-

94

Nov-

95

Nov-

96

Nov-

97

Nov-

98

Nov-

99

Nov-

00

Nov-

01

Nov-

02

Nov-

03

Nov-

04

Nov-

05

Nov-

06

Nov-

07

Nov-

08

Nov-

09


-80%-60%-40%-20%

0%20%40%60%

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09



1 1.2% 14.3% 5% 66%10 1.1% 10.1% 7% 43%


Universe 0.8% 1.2% 1.4% 2245


Long/Short 0.2% 0.01% 5.5% 61%Avg


Total Test



0100200300

400500600

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09


61




Growth/Earnings Composite Forward Earnings Momentum – Long/Short

We calculate the change in FY1consensus EPS over the last month and the past 3 months and the change in FY2 consensus EPS over the past month and the past 3 months. The final Composite Earnings Momentum Factor is taken as an average of the above 4 metrics. Stocks with the highest Earnings Momentum are assigned to portfolio, lowest to portfolio 10.


0%2%4%6%8%

10%12%14%16%

1 2 3 4 5 6 7 8 9 10


-30%

-20%

-10%

0%

10%

20%

Dec-

93

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Cumulative Returns

050

100150200250300350400

Base

Nov-

94

Nov-

95

Nov-

96

Nov-

97

Nov-

98

Nov-

99

Nov-

00

Nov-

01

Nov-

02

Nov-

03

Nov-

04

Nov-

05

Nov-

06

Nov-

07

Nov-

08

Nov-

09


-40%-30%-20%-10%

0%10%20%30%40%

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09



1 1.2% 13.6% 5% 68%10 0.7% 5.7% 6% 45%


Universe 0.8% 2.1% 1.6% 2033


Long/Short 0.5% 6.22% 2.8% 64%Avg


Total Test



0100200300

400500600

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09


62




Quality Historical ROE – Long/Short

Stocks are ranked on their historical Return on Equity (ROE). Stocks with the highest ROE are allocated to portfolio 1, lowest to portfolio 10.


0%

2%

4%

6%

8%

10%

12%

1 2 3 4 5 6 7 8 9 10


-20%-15%-10%

-5%0%5%

10%15%20%

Dec-

93

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Cumulative Returns

0

50

100

150

200

Base

Nov-

94

Nov-

95

Nov-

96

Nov-

97

Nov-

98

Nov-

99

Nov-

00

Nov-

01

Nov-

02

Nov-

03

Nov-

04

Nov-

05

Nov-

06

Nov-

07

Nov-

08

Nov-

09


-50%-40%-30%-20%-10%

0%10%20%30%

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09



1 0.9% 10.3% 5% 51%10 0.8% 7.4% 6% 45%


Universe 0.8% 1.6% 0.8% 2300


Long/Short 0.1% 1.13% 3.2% 61%Avg


Total Test



050

100150

200250300

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09


63




Multi Factor Model Blended combination of Price, Value, Growth and Quality – Long/Short

Stocks are ranked according to their Scores on our Multi Factor Quant Model. Stocks with the highest ranking are allocated to portfolio 1, lowest to portfolio 10.


0%

5%

10%

15%

20%

1 2 3 4 5 6 7 8 9 10


-30%

-20%

-10%

0%

10%

20%

30%

Dec-

93

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09

Cumulative Returns

0

200

400

600

800

1,000

Base

Nov-

94

Nov-

95

Nov-

96

Nov-

97

Nov-

98

Nov-

99

Nov-

00

Nov-

01

Nov-

02

Nov-

03

Nov-

04

Nov

05


-40%

-20%

0%

20%

40%

60%

Dec-

94

Dec-

95

Dec-

96

Dec-

97

Dec-

98

Dec-

99

Dec-

00

Dec-

01

Dec-

02

Dec-

03

Dec-

04

Dec-

05

Dec-

06

Dec-

07

Dec-

08

Dec-

09



1 1.5% 17.4% 5% 73%10 0.4% 3.0% 6% 34%


Universe 0.8% 3.8% 3.3% 2038


Long/Short 1.0% 12.50% 3.2% 73%Avg



Total Test


0100200300400500600700800

Dec-93

Dec-95

Dec-97

Dec-99

Dec-01

Dec-03

Dec-05

Dec-07

Dec-09


64




Current Long Opportunities Below we highlight the top 25 long opportunities arising from the Multi-Factor Model defined above.

Table 77: Long Opportunities, Multi-Factor Model – MSCI AC World BBG Name Country Sector Model Score

WDC US WESTERN DIGITAL UNITED STATES Information Technology 3.00 VTBR RM VTB BANK OJSC RUSSIA Financials 3.00 VED LN VEDANTA RESOURCES BRITAIN Materials 2.88 SESA IN SESA GOA INDIA Materials 2.87 2338 HK WEICHAI POWER CHINA Industrials 2.81 ITMG IJ INDO TAMBANGRAYA MEGAH INDONESIA Energy 2.81 CPF TB CHAROEN POKPHAND FOODS PUB THAILAND Consumer Staples 2.76 STX US SEAGATE TECHNOLOGY CAYMAN ISLANDS Information Technology 2.73 XEC US CIMAREX ENERGY UNITED STATES Energy 2.66 8001 JP ITOCHU JAPAN Industrials 2.62 ASII IJ ASTRA INTERNATIONAL INDONESIA Consumer Discretionary 2.58 ACI US ARCH COAL UNITED STATES Energy 2.57 UOL SP UOL SINGAPORE Financials 2.56 TSN US TYSON FOODS UNITED STATES Consumer Staples 2.52 6665 JP ELPIDA MEMORY JAPAN Information Technology 2.50 IFX GR INFINEON TECHNOLOGIES GERMANY Information Technology 2.48 WCRX US WARNER CHILCOTT IRELAND Health Care 2.39 THYAO TI TURK HAVA YOLLARI AO TURKEY Industrials 2.33 8002 JP MARUBENI JAPAN Industrials 2.30 5214 JP NIPPON ELECTRIC GLASS JAPAN Information Technology 2.28 HSY US HERSHEY UNITED STATES Consumer Staples 2.27 3900 HK GREENTOWN CHINA CHINA Financials 2.26 SSA LI SISTEMA JSFC RUSSIA Telecommunication Services 2.24 SLM US SLM UNITED STATES Financials 2.23 751 HK SKYWORTH DIGITAL HLDGS HONG KONG Consumer Discretionary 2.23 Source: Factset, J.P. Morgan

Table 78: Long Opportunities, Multi-Factor Model, MSCI Europe BBG Name Country Sector Model Score

VED LN VEDANTA RESOURCES PLC BRITAIN Materials 2.88 IFX GR INFINEON TECHNOLOGIES AG GERMANY Information Technology 2.48 OML LN OLD MUTUAL PLC BRITAIN Financials 2.13 SKFB SS SKF AB-B SHARES SWEDEN Industrials 2.02 PP FP PPR FRANCE Consumer Discretionary 1.97 NXT LN NEXT PLC BRITAIN Consumer Discretionary 1.92 ORNBV FH ORION OYJ-CLASS B FINLAND Health Care 1.82 AV/ LN AVIVA PLC BRITAIN Financials 1.79 VPK NA VOPAK NETHERLANDS Industrials 1.78 STERV FH STORA ENSO OYJ-R SHS FINLAND Materials 1.76 BATS LN BRITISH AMERICAN TOBACCO PLC BRITAIN Consumer Staples 1.71 FUM1V FH FORTUM OYJ FINLAND Utilities 1.71 TIT IM TELECOM ITALIA SPA ITALY Telecommunication Services 1.70 JMT PL JERONIMO MARTINS PORTUGAL Consumer Staples 1.69 UMI BB UMICORE BELGIUM Materials 1.67 BT/A LN BT GROUP PLC BRITAIN Telecommunication Services 1.65 CNA LN CENTRICA PLC BRITAIN Utilities 1.63 LUN DC H LUNDBECK A/S DENMARK Health Care 1.60 LGEN LN LEGAL & GENERAL GROUP PLC BRITAIN Financials 1.57 ISYS LN INVENSYS PLC BRITAIN Industrials 1.56 KAZ LN KAZAKHMYS PLC BRITAIN Materials 1.55 DBK GR DEUTSCHE BANK AG-REGISTERED GERMANY Financials 1.52 HEIO NA HEINEKEN HOLDING NV NETHERLANDS Consumer Staples 1.51 KN FP NATIXIS FRANCE Financials 1.46 ULVR LN UNILEVER PLC BRITAIN Consumer Staples 1.44 Source: Factset, J.P. Morgan

Regarding our Long/Short opportunities, past performance is not indicative of future results. These are theoretical screens that should not be regarded as implicit or explicit recommendations. The stocks are selected on the basis of factual or consensus data; J.P. Morgan has not undertaken fundamental analysis on the names outside of our coverage universe.

65




Appendix III: Z-Score Normalisation - Getting Technical!

To reach a ranking for a stock, we typically convert stock level data to a Quantitative Factor by a process called Z-Score normalisation.

For Each stock, we deduct the universe average and divide this by the universe standard deviation.

Consequently if a stock has a Z-Score of 2 we can say that it is 2 standard deviations above the norm. We often truncate Z-Scores at +/- 3 as larger values can be distortive and are often the result of a data error.

Figure 46: Z-Score Normalisation – The Bell Curve

-3 -2 -1 0 1 2 3Standard Deviation(s)

Source: J.P. Morgan

By normalizing the raw data, we can combine many different Factors or ideas and consequently combine scores to create a multi-Factor model

e.g.

QuantFactor = DYi + PMi 2

66




Where DY is Dividend Yield and PM is Price Momentum

Generally, we can assign different weights (k) to each Quant Factor (QF) when creating a multi-Factor Model consisting of n individual Factors:

QuantAlphaModeli = k1QF1,i + k2QF2,i +…..+ knQFn,i

We can then quantify the usefulness of a signal over time by looking at a simple long/short model for the Factor going long the top ntile and short the bottom ntile according to the underlying Factor score.

i.e. For deciles, long top 10%, short bottom 10%

67




Analyst Certification: The research analyst(s) denoted by an “AC” on the cover of this report certifies (or, where multiple research analysts are primarily responsible for this report, the research analyst denoted by an “AC” on the cover or within the document individually certifies, with respect to each security or issuer that the research analyst covers in this research) that: (1) all of the views expressed in this report accurately reflect his or her personal views about any and all of the subject securities or issuers; and (2) no part of any of the research analyst’s compensation was, is, or will be directly or indirectly related to the specific recommendations or views expressed by the research analyst(s) in this report.

Important Disclosures

Explanation of Equity Research Ratings and Analyst(s) Coverage Universe: J.P. Morgan uses the following rating system: Overweight [Over the next six to twelve months, we expect this stock will outperform the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] Neutral [Over the next six to twelve months, we expect this stock will perform in line with the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] Underweight [Over the next six to twelve months, we expect this stock will underperform the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] J.P. Morgan Cazenove’s UK Small/Mid-Cap dedicated research analysts use the same rating categories; however, each stock’s expected total return is compared to the expected total return of the FTSE All Share Index, not to those analysts’ coverage universe. A list of these analysts is available on request. The analyst or analyst’s team’s coverage universe is the sector and/or country shown on the cover of each publication. See below for the specific stocks in the certifying analyst(s) coverage universe.

J.P. Morgan Equity Research Ratings Distribution, as of March 31, 2010

Overweight (buy)

Neutral (hold)

Underweight (sell)

JPM Global Equity Research Coverage 45% 42% 13% IB clients* 48% 46% 32% JPMSI Equity Research Coverage 42% 49% 10% IB clients* 70% 58% 48%

*Percentage of investment banking clients in each rating category. For purposes only of NASD/NYSE ratings distribution rules, our Overweight rating falls into a buy rating category; our Neutral rating falls into a hold rating category; and our Underweight rating falls into a sell rating category.

Valuation and Risks: Please see the most recent company-specific research report for an analysis of valuation methodology and risks on any securities recommended herein. Research is available at http://www.morganmarkets.com , or you can contact the analyst named on the front of this note or your J.P. Morgan representative.

Analysts’ Compensation: The equity research analysts responsible for the preparation of this report receive compensation based upon various factors, including the quality and accuracy of research, client feedback, competitive factors, and overall firm revenues, which include revenues from, among other business units, Institutional Equities and Investment Banking.

Registration of non-US Analysts: Unless otherwise noted, the non-US analysts listed on the front of this report are employees of non-US affiliates of JPMSI, are not registered/qualified as research analysts under NASD/NYSE rules, may not be associated persons of JPMSI, and may not be subject to NASD Rule 2711 and NYSE Rule 472 restrictions on communications with covered companies, public appearances, and trading securities held by a research analyst account.

Other Disclosures

J.P. Morgan is the global brand name for J.P. Morgan Securities Inc. (JPMSI) and its non-US affiliates worldwide. J.P. Morgan Cazenove is a brand name for equity research produced by J.P. Morgan Securities Ltd.; J.P. Morgan Equities Limited; JPMorgan Chase Bank, N.A., Dubai Branch; and J.P. Morgan Bank International LLC.

Options related research: If the information contained herein regards options related research, such information is available only to persons who have received the proper option risk disclosure documents. For a copy of the Option Clearing Corporation’s Characteristics and Risks of Standardized Options, please contact your J.P. Morgan Representative or visit the OCC’s website at http://www.optionsclearing.com/publications/risks/riskstoc.pdf.

Legal Entities Disclosures U.S.: JPMSI is a member of NYSE, FINRA and SIPC. J.P. Morgan Futures Inc. is a member of the NFA. JPMorgan Chase Bank, N.A. is a

http://www.morganmarkets.com/

68




member of FDIC and is authorized and regulated in the UK by the Financial Services Authority. U.K.: J.P. Morgan Securities Ltd. (JPMSL) is a member of the London Stock Exchange and is authorised and regulated by the Financial Services Authority. Registered in England & Wales No. 2711006. Registered Office 125 London Wall, London EC2Y 5AJ. South Africa: J.P. Morgan Equities Limited is a member of the Johannesburg Securities Exchange and is regulated by the FSB. Hong Kong: J.P. Morgan Securities (Asia Pacific) Limited (CE number AAJ321) is regulated by the Hong Kong Monetary Authority and the Securities and Futures Commission in Hong Kong. Korea: J.P. Morgan Securities (Far East) Ltd, Seoul Branch, is regulated by the Korea Financial Supervisory Service. Australia: J.P. Morgan Australia Limited (ABN 52 002 888 011/AFS Licence No: 238188) is regulated by ASIC and J.P. Morgan Securities Australia Limited (ABN 61 003 245 234/AFS Licence No: 238066) is a Market Participant with the ASX and regulated by ASIC. Taiwan: J.P.Morgan Securities (Taiwan) Limited is a participant of the Taiwan Stock Exchange (company-type) and regulated by the Taiwan Securities and Futures Bureau. India: J.P. Morgan India Private Limited is a member of the National Stock Exchange of India Limited and Bombay Stock Exchange Limited and is regulated by the Securities and Exchange Board of India. Thailand: JPMorgan Securities (Thailand) Limited is a member of the Stock Exchange of Thailand and is regulated by the Ministry of Finance and the Securities and Exchange Commission. Indonesia: PT J.P. Morgan Securities Indonesia is a member of the Indonesia Stock Exchange and is regulated by the BAPEPAM LK. Philippines: J.P. Morgan Securities Philippines Inc. is a member of the Philippine Stock Exchange and is regulated by the Securities and Exchange Commission. Brazil: Banco J.P. Morgan S.A. is regulated by the Comissao de Valores Mobiliarios (CVM) and by the Central Bank of Brazil. Mexico: J.P. Morgan Casa de Bolsa, S.A. de C.V., J.P. Morgan Grupo Financiero is a member of the Mexican Stock Exchange and authorized to act as a broker dealer by the National Banking and Securities Exchange Commission. Singapore: This material is issued and distributed in Singapore by J.P. Morgan Securities Singapore Private Limited (JPMSS) [MICA (P) 020/01/2010 and Co. Reg. No.: 199405335R] which is a member of the Singapore Exchange Securities Trading Limited and is regulated by the Monetary Authority of Singapore (MAS) and/or JPMorgan Chase Bank, N.A., Singapore branch (JPMCB Singapore) which is regulated by the MAS. Malaysia: This material is issued and distributed in Malaysia by JPMorgan Securities (Malaysia) Sdn Bhd (18146-X) which is a Participating Organization of Bursa Malaysia Berhad and a holder of Capital Markets Services License issued by the Securities Commission in Malaysia. Pakistan: J. P. Morgan Pakistan Broking (Pvt.) Ltd is a member of the Karachi Stock Exchange and regulated by the Securities and Exchange Commission of Pakistan. Saudi Arabia: J.P. Morgan Saudi Arabia Ltd. is authorised by the Capital Market Authority of the Kingdom of Saudi Arabia (CMA) to carry out dealing as an agent, arranging, advising and custody, with respect to securities business under licence number 35-07079 and its registered address is at 8th Floor, Al-Faisaliyah Tower, King Fahad Road, P.O. Box 51907, Riyadh 11553, Kingdom of Saudi Arabia. Dubai: JPMorgan Chase Bank, N.A., Dubai Branch is regulated by the Dubai Financial Services Authority (DFSA) and its registered address is Dubai International Financial Centre - Building 3, Level 7, PO Box 506551, Dubai, UAE.

Country and Region Specific Disclosures U.K. and European Economic Area (EEA): Unless specified to the contrary, issued and approved for distribution in the U.K. and the EEA by JPMSL. Investment research issued by JPMSL has been prepared in accordance with JPMSL's policies for managing conflicts of interest arising as a result of publication and distribution of investment research. Many European regulators require that a firm to establish, implement and maintain such a policy. This report has been issued in the U.K. only to persons of a kind described in Article 19 (5), 38, 47 and 49 of the Financial Services and Markets Act 2000 (Financial Promotion) Order 2005 (all such persons being referred to as "relevant persons"). This document must not be acted on or relied on by persons who are not relevant persons. Any investment or investment activity to which this document relates is only available to relevant persons and will be engaged in only with relevant persons. In other EEA countries, the report has been issued to persons regarded as professional investors (or equivalent) in their home jurisdiction. Australia: This material is issued and distributed by JPMSAL in Australia to “wholesale clients” only. JPMSAL does not issue or distribute this material to “retail clients.” The recipient of this material must not distribute it to any third party or outside Australia without the prior written consent of JPMSAL. For the purposes of this paragraph the terms “wholesale client” and “retail client” have the meanings given to them in section 761G of the Corporations Act 2001. Germany: This material is distributed in Germany by J.P. Morgan Securities Ltd., Frankfurt Branch and J.P.Morgan Chase Bank, N.A., Frankfurt Branch which are regulated by the Bundesanstalt für Finanzdienstleistungsaufsicht. Hong Kong: The 1% ownership disclosure as of the previous month end satisfies the requirements under Paragraph 16.5(a) of the Hong Kong Code of Conduct for Persons Licensed by or Registered with the Securities and Futures Commission. (For research published within the first ten days of the month, the disclosure may be based on the month end data from two months’ prior.) J.P. Morgan Broking (Hong Kong) Limited is the liquidity provider for derivative warrants issued by J.P. Morgan Structured Products B.V. and listed on the Stock Exchange of Hong Kong Limited. An updated list can be found on HKEx website: http://www.hkex.com.hk/prod/dw/Lp.htm. Japan: There is a risk that a loss may occur due to a change in the price of the shares in the case of share trading, and that a loss may occur due to the exchange rate in the case of foreign share trading. In the case of share trading, JPMorgan Securities Japan Co., Ltd., will be receiving a brokerage fee and consumption tax (shouhizei) calculated by multiplying the executed price by the commission rate which was individually agreed between JPMorgan Securities Japan Co., Ltd., and the customer in advance. Financial Instruments Firms: JPMorgan Securities Japan Co., Ltd., Kanto Local Finance Bureau (kinsho) No. 82 Participating Association / Japan Securities Dealers Association, The Financial Futures Association of Japan. Korea: This report may have been edited or contributed to from time to time by affiliates of J.P. Morgan Securities (Far East) Ltd, Seoul Branch. Singapore: JPMSS and/or its affiliates may have a holding in any of the securities discussed in this report; for securities where the holding is 1% or greater, the specific holding is disclosed in the Important Disclosures section above. India: For private circulation only, not for sale. Pakistan: For private circulation only, not for sale. New Zealand: This material is issued and distributed by JPMSAL in New Zealand only to persons whose principal business is the investment of money or who, in the course of and for the purposes of their business, habitually invest money. JPMSAL does not issue or distribute this material to members of "the public" as determined in accordance with section 3 of the Securities Act 1978. The recipient of this material must not distribute it to any third party or outside New Zealand without the prior written consent of JPMSAL. Canada: The information contained herein is not, and under no circumstances is to be construed as, a prospectus, an advertisement, a public offering, an offer to sell securities described herein, or solicitation of an offer to buy securities described herein, in Canada or any province or territory thereof. Any offer or sale of the securities described herein in Canada will be made only under an exemption from the requirements to file a prospectus with the relevant Canadian securities regulators and only by a dealer properly registered under applicable securities laws or, alternatively, pursuant to an exemption from the dealer registration requirement in the relevant province or territory of Canada in which such offer or sale is made. The information contained herein is under no circumstances to

69




be construed as investment advice in any province or territory of Canada and is not tailored to the needs of the recipient. To the extent that the information contained herein references securities of an issuer incorporated, formed or created under the laws of Canada or a province or territory of Canada, any trades in such securities must be conducted through a dealer registered in Canada. No securities commission or similar regulatory authority in Canada has reviewed or in any way passed judgment upon these materials, the information contained herein or the merits of the securities described herein, and any representation to the contrary is an offence. Dubai: This report has been issued to persons regarded as professional clients as defined under the DFSA rules.

General: Additional information is available upon request. Information has been obtained from sources believed to be reliable but JPMorgan Chase & Co. or its affiliates and/or subsidiaries (collectively J.P. Morgan) do not warrant its completeness or accuracy except with respect to any disclosures relative to JPMSI and/or its affiliates and the analyst’s involvement with the issuer that is the subject of the research. All pricing is as of the close of market for the securities discussed, unless otherwise stated. Opinions and estimates constitute our judgment as of the date of this material and are subject to change without notice. Past performance is not indicative of future results. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The opinions and recommendations herein do not take into account individual client circumstances, objectives, or needs and are not intended as recommendations of particular securities, financial instruments or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. JPMSI distributes in the U.S. research published by non-U.S. affiliates and accepts responsibility for its contents. Periodic updates may be provided on companies/industries based on company specific developments or announcements, market conditions or any other publicly available information. Clients should contact analysts and execute transactions through a J.P. Morgan subsidiary or affiliate in their home jurisdiction unless governing law permits otherwise.

“Other Disclosures” last revised March 1, 2010.

Copyright 2010 JPMorgan Chase & Co. All rights reserved. This report or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan.

31537878 jp-morgan-quantitative-guide-to-world-cup-2010

Sports

Transcript of 31537878 jp-morgan-quantitative-guide-to-world-cup-2010